{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MohammedZ666/ECGNet/blob/main/training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ns8Kmq2aI-d0"
      },
      "source": [
        "# Dowloading ECG data from [MIT-BIH Arrhythmia Database](https://physionet.org/content/mitdb/1.0.0/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8LrRD8jKLDE"
      },
      "source": [
        "Important links\n",
        "* https://leimao.github.io/article/Neural-Networks-Quantization/\n",
        "* https://www.quora.com/What-is-the-difference-between-a-gradient-and-a-derivative\n",
        "* https://towardsdatascience.com/why-gradient-descent-is-so-common-in-data-science-def3e6515c5c\n",
        "* https://arxiv.org/pdf/1803.08375.pdf\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TV0dKNct-yFR"
      },
      "outputs": [],
      "source": [
        "# !wget -r -N -c -np https://physionet.org/files/mitdb/1.0.0/\n",
        "# !rm -rf mitdb\n",
        "# !mkdir mitdb\n",
        "# !mv  physionet.org/files/mitdb/1.0.0/* mitdb\n",
        "# !rm -rf physionet.org\n",
        "# !pip install wfdb\n",
        "# import wfdb\n",
        "# from wfdb import processing\n",
        "# https://winlibs.com/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjm7QMkzLReZ"
      },
      "source": [
        "# Installing and importing dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "maUyr-3mLHxi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dd7ecd9-79e3-42e4-a1ff-9d3840f0e7fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.20.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (591 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m591.0/591.0 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow_addons) (23.1)\n",
            "Collecting typeguard<3.0.0,>=2.7 (from tensorflow_addons)\n",
            "  Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n",
            "Installing collected packages: typeguard, tensorflow_addons\n",
            "Successfully installed tensorflow_addons-0.20.0 typeguard-2.13.3\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow_addons\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from random import randint\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "from scipy.signal import butter, lfilter\n",
        "from time import time\n",
        "from google.colab import drive\n",
        "# drive.mount('/content/gdrive')\n",
        "# PATH='/content/gdrive/MyDrive/ECG_ML'\n",
        "PATH = '/content/'\n",
        "all_types = ['N', 'S', 'V', 'F']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JpppCc6QoClN"
      },
      "source": [
        "# Concentrating heartbeats by the PT process and saving as CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lBEmSMeTljXF"
      },
      "outputs": [],
      "source": [
        "# # N includes beats originating in the sinus node (normal and bundle branch block beat types);\n",
        "# # S includes supraventricular ectopic beats (SVEBs);\n",
        "# # V includes ventricular ectopic beats (VEBs);\n",
        "# # F includes beats that result from fusing normal and VEBs;\n",
        "# # Q includes any heartbeat not in the N, S, V, or F categories, essentially the undefined heartbeats\n",
        "# # as well as beats resulting from the use of a pacemaker. Lets leave this out\n",
        "# # Assuming the heart beats 100 times in a minute we take 1 heart_beat == 100/60,\n",
        "# # therefore taking 100/(60*2) seconds of values to the left and right of the peak\n",
        "\n",
        "\n",
        "# def rdbeats(patient):\n",
        "#   path = '%s/mitdb/%d'% (os.getcwd(), patient)\n",
        "#   rows = pd.DataFrame()\n",
        "#   try:\n",
        "#     record = wfdb.rdsamp(path)[0][:,0]\n",
        "#   except Exception as e :\n",
        "#     print(e)\n",
        "#     return pd.DataFrame()\n",
        "#   ann = wfdb.rdann(path, 'atr')\n",
        "#   beat_data = []\n",
        "\n",
        "#   for i, symbol in enumerate(ann.symbol):\n",
        "#     if symbol in n_beats:\n",
        "#       beat_data.append(('N', ann.sample[i]))\n",
        "#     elif symbol in s_beats:\n",
        "#       beat_data.append(('S', ann.sample[i]))\n",
        "#     elif symbol in v_beats:\n",
        "#       beat_data.append(('V', ann.sample[i]))\n",
        "#     elif symbol in f_beats:\n",
        "#       beat_data.append(('F', ann.sample[i]))\n",
        "\n",
        "\n",
        "#   for datum in beat_data:\n",
        "#     i = datum[1]\n",
        "#     start = i - int((samplen/2))\n",
        "#     end = i + int((samplen/2))\n",
        "#     if start < 0:\n",
        "#       zeros = start * -1\n",
        "#       beat = record[0:int(samplen/2)+i]\n",
        "#       beat = np.insert(beat, 0, np.zeros(zeros))\n",
        "#     elif end > (len(record)-1):\n",
        "#       zeros = end - (len(record)-1)\n",
        "#       beat = record[start: len(record)]\n",
        "#       beat = np.concatenate((beat, np.zeros(zeros)))\n",
        "#     else:\n",
        "#       beat = record[start:start+samplen]\n",
        "\n",
        "#     beat = resample(beat, resamp_to, beat_time)\n",
        "#     oribeat = beat\n",
        "#     beat = pt_preprocess(beat, resamp_to)\n",
        "#     row = {'patient':[patient], 'beat' : np.array2string(oribeat, separator=', ')}\n",
        "\n",
        "#     # One hot encoding the types\n",
        "#     for t in all_types:\n",
        "#       row[t] = [1] if t == datum[0] else [0]\n",
        "\n",
        "#     # Adding beats\n",
        "#     for i, b in enumerate(beat):\n",
        "#       row[str(i)] = [b]\n",
        "#     row = pd.DataFrame(row)\n",
        "#     rows = pd.concat([rows,row])\n",
        "\n",
        "#   return rows\n",
        "\n",
        "# def resample(x, to_sample_hz, total_time):\n",
        "#     new_len = int(to_sample_hz * 0.6)\n",
        "#     sample_diff = x.size - new_len\n",
        "#     delete_every = int(x.size/sample_diff)\n",
        "#     x = np.delete(x, np.arange(0, x.size, delete_every))\n",
        "#     start = True\n",
        "#     for z in range(new_len - x.size):\n",
        "#       if start:\n",
        "#         x = np.insert(x, 0, 0)\n",
        "#       else:\n",
        "#         x = np.append(x, 0)\n",
        "#       start = not start\n",
        "#     return x\n",
        "\n",
        "\n",
        "# def pt_preprocess(beat, samp_size):\n",
        "#     integration_window = 15\n",
        "#     low_hz = 5\n",
        "#     hi_hz = 15\n",
        "#     order = 1\n",
        "#     filtered_beat = bandpass_filter(beat, low_hz, hi_hz, samp_size, order)\n",
        "#     diffed_beat = np.ediff1d(filtered_beat)\n",
        "#     sqaured_beat = diffed_beat ** 2\n",
        "#     convolved_beat = np.convolve(sqaured_beat, np.ones(integration_window))\n",
        "#     return convolved_beat\n",
        "\n",
        "\n",
        "# def bandpass_filter(data, lowcut, highcut, signal_freq, filter_order):\n",
        "#     nyquist_freq = 0.5 * signal_freq\n",
        "#     low = lowcut / nyquist_freq\n",
        "#     high = highcut / nyquist_freq\n",
        "#     b, a = butter(filter_order, [low, high], btype=\"band\")\n",
        "#     y = lfilter(b, a, data)\n",
        "#     return y\n",
        "\n",
        "# n_beats = ['N', 'L', 'R', 'e', 'j']\n",
        "# s_beats = ['A', 'a', 'J', 'S']\n",
        "# v_beats = ['V', 'E']\n",
        "# f_beats = ['F']\n",
        "# resamp_to = 250\n",
        "# beat_time = 0.6\n",
        "# samplen = int(beat_time * 360)\n",
        "\n",
        "# # if samplen%2 != 0 : samplen+=1\n",
        "# df = pd.DataFrame()\n",
        "\n",
        "# for i in range(100, 235):\n",
        "#   df= pd.concat([df, rdbeats(i)], ignore_index=True)\n",
        "\n",
        "# df.to_csv('%s/mit_bih.csv'%(PATH), index=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown \"https://drive.google.com/uc?id=1-6oaGPNnMuT9xppRz6HNMZHETpzQCOw0&confirm=t&uuid=af1e3755-329a-4108-a633-9ee04916f7a7&at=AHV7M3eHzCwdgZlry-jTdzJ5vH2L:1669554255041\"\n",
        "PATH = \"/content/\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JD2j-fc3DpVb",
        "outputId": "11906eff-13d2-4346-ced5-6f57294d2f21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-6oaGPNnMuT9xppRz6HNMZHETpzQCOw0&confirm=t&uuid=af1e3755-329a-4108-a633-9ee04916f7a7&at=AHV7M3eHzCwdgZlry-jTdzJ5vH2L:1669554255041\n",
            "To: /content/mit_bih.csv\n",
            "100% 471M/471M [00:07<00:00, 63.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-PFvhx7WocIh"
      },
      "source": [
        "# Visualizing concentrated data for each type of beat N, S, V and F\n",
        "\n",
        "![image.png]()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "txcqy1mfyud1",
        "outputId": "7a761d73-1c21-4743-a7ce-cfb3cd7dad0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "beat type N\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "beat type S\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "beat type V\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "beat type F\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# !rm -rf /content/mit_bih.csv\n",
        "# !gdown --id 1-3tvbqnwQx1quxvdN_M9G4iq6afa5RuB\n",
        "plt.rc('xtick', labelsize='33')    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize='33')\n",
        "\n",
        "\n",
        "df = pd.read_csv('%s/mit_bih.csv'%(PATH))\n",
        "\n",
        "for index, symbol in enumerate(all_types):\n",
        "  new_df = df[df[symbol]==1]\n",
        "  index = randint(0, new_df.shape[0]-1)\n",
        "  oribeat =  eval((new_df.iloc[index]['beat']))\n",
        "  preprocessed  = new_df.iloc[index]['0':df.columns[-1]]\n",
        "\n",
        "  print(f\"\\nbeat type {symbol}\\n\")\n",
        "  fig, axes = plt.subplots(1,2, figsize=(16,9), constrained_layout = True)\n",
        "  axes[0].plot(np.arange(0, 163),preprocessed)\n",
        "  axes[0].set_xlabel(\"Time (seconds)\", size=33)\n",
        "  axes[0].set_ylabel(\"Units\", size=33)\n",
        "  axes[1].plot(oribeat)\n",
        "  axes[1].set_xlabel(\"Time (seconds)\", size=33)\n",
        "  axes[1].set_ylabel(\"Voltage (mV)\", size=33)\n",
        "  plt.savefig(f\"{PATH}/{symbol}_beat.png\")\n",
        "  plt.show()\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zcNrkwtIFJFL"
      },
      "source": [
        "# Only keeping concetrated part of the beat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6N18YiDg5QHi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d31e2e91-2727-4313-e8b3-c6916cf5d530"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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IjY3Fjh07EBERYXaf9gar+vp6zJw5Ez/++KPZfa40cOBAbNy4EcHBwZL6M1gROT9BEPDb8Yt467eTSL/QEKg0aiXq9Ea4q5XY/PeR6BrMEW8iR9LhtwIb+fr64tdff8XUqVMt6jdixAjs3LnTolBlDW5ubli5ciXmz59v8b6Ef/3rX7F9+3bJoYqInJsgCNiSeRFT3t2Jez7fj/QLFfByV+Fvo2Oxe+FYjOgZDJ3eiGd+TJO0SDER2T+r7Hjs6+uL1atXY82aNRg6dOhVX5uQkIBPP/0U27ZtQ1hYmDUubzG1Wo3Fixdj3759mD59Otzd3Vt9raenJ1JSUrB79258+eWXNpkLRkSOp6pOj1kf7Madn+7D0fPl8HRT4YHkWPzx5Bg8MaEPArzd8dIt/aBRK7Hz1CX8ePiC3CUTUQdo963AluTl5WH37t04c+YMtFotPD09ERUVhcGDB6N79+7Wvly7abVapKam4uTJkygrK4ObmxtCQkIQExODpKQkeHh4WO1avBVI5Jw+3ZmDf63LgIebEnOSuuK+kd0R7KNp9rp3t5zCfzadQKC3O36bn4wA79Z/sSMi+2GzOVZkGQYrIuc095O92HayCE9P7oP7Rsa2+jqd3oiblu7AycIqzBzUBa9Pb77lFhHZH5vNsSIicnU1OgN2n74EABjV++o7SLirlXhlasO+qN/tP9/Uj4icA4MVEVE77T59CXV6Izp38kTP0NYXSG40qGsg/nJdNADgmR/SUKc3dHSJRGQjDFZERO209cRFAEBy75BmS8K05qmJfRDso0F2kRbvbz3dkeURkQ0xWBERtYMgCNhyoggAMLqN24BX8vdyw3M39wXQMKH9dFFVh9RHRLbFYEVE1A45xVqcK6mGu0qJobFBFvW9OSECI3uFQGcw4pkf0rm2FZETYLAiImqHxtGq67oFwltj3tZcjRQKBV6+pR883JRIPX0J3x8433YnIrJrDFZERO3QOL9qVO8QSf2jAr3w2NheAIC3f8uC0chRKyJHxmBFRCRRtU6PPadLALS9zMLV3DmsK3w91DhfWoNd2Vx+gciRMVgREUmUmn0JOoMRXQI8ERsifVNlDzcVbrmmMwDgm33nrFUeEcmAwYqISKItl28Dju4davYyC62ZNTgKALD5WCFKtbp210ZE8mCwIiKSQBAEbL08cV3q/Kor9evsj/hIP+gMRm7QTOTAGKyIiCTILqrC+dIauKuUSLJwmYXWNI5afbsvl0svEDkoBisiIgkaR6uGdA+El7tlyyy0ZsqAznBXK5FZUImj58utck4isi0GKyIiCbY0LbMg/WlAU/5ebpjcLxwA8M2+XKudl4hsh8GKiMhC2jo99uY0LLMw2grzq6408/LtwHVH8lCt01v13ETU8RisiIgstPNUMeoNAqIDvdAtWPoyCy25vlsQYoK8UFWnx89pBVY9NxF1PAYrIiILbT3ZuOlySLuXWTClVCowc1DjJHauaUXkaBisiIgsIAgCtmZaf37VlW69tguUCmDfmVJkF1V1yDWIqGMwWBERWSDrYhXyymuhUStxfXfrLLNgKtzfA6Mvh7bvOImdyKEwWBERWaBx0+XruwfB013VYddpnMS+6uB51BuMHXYdIrIuBisiIgtsyfxzflVHGtMnFME+GhRX6fD75VuPRGT/GKyIiMxUWVuP/WcbllnoqPlVjdxUStw6sGFj5m95O5DIYTBYERGZaeepS6g3COgW7I2uVl5moSWNTwduPXERBeW1HX49Imo/BisiIjNtO9lwSy65V8feBmwUG+KD67oGwigA3x/gqBWRI2CwIiIy0+7TDbcBbRWsgD8nsX+3/zyMRm7MTGTvGKyIiMxQW2/A2UtaAEB8Zz+bXXdy/3D4atQ4V1KN3TmXbHZdIpKGwYqIyAw5xVoYBcDPQ40QH43NruvlrsZNAyIBAKsPXrDZdYlIGgYrIiIzZF1sWAG9Z5iv1bexacut1zY8HbghLZ8bMxPZOQYrIiIznLocrHqE+Nj82gNjAhAd6AWtzoDNxwptfn0iMh+DFRGRGU5drAQA9AyzfbBSKBSYmtgwarX6EG8HEtkzBisiIjM0jljFhto+WAHAtMu3A//IKkJhBde0IrJXDFZERG3QG4zIKW54IrCnTMEqJsgbg2ICYBSANYc5akVkrxisiIjacLakGvUGAZ5uKkT6e8pWx7RruwAAVh24AEHgmlZE9ojBioioDVmFlyeuh/pAqbTtE4FXurF/BNzVSpworERGfoVsdRBR6xisiIjakF30Z7CSk7+XG8bHhQHgmlZE9orBioioDVmFDU8Eyh2sgD8nsa85fAF6g1HmaojIFIMVEVEbTtnJiBUAjOwVgiBvdxRX6bAjq1jucojIBIMVEdFVGI1C01ILcj0ReCU3lRI3X97iZtXB8zJXQ0SmGKyIiK7iQlkNauuNcFcpER3oJXc5AIBbLz8duDmjEBW19TJXQ0RXYrAiIrqKxtGqbsHeUKvs40dmv85+6BnqA53eiA1p+XKXQ0RXsI+fEkREdqppj0A7uA3YSKFQ/LmmFZ8OJLIrDFZERFeRdXmPQLm2smnNLYmRUCiAvTklyC2plrscIrqMwYqI6CrsaeL6lSL8PTEsNhgA8AM3ZiayGwxWREStEATBLm8FNmpc02r1wfPc4obITjBYERG1oqiyDhW1eigVDZPX7c2E+HB4uqlw5lI1Dp4rk7scIgKDFRFRqxpHq6IDveDhppK5mua8NWpM6hcOgGtaEdkLBisiolZkNd0G9JW5ktbdOvDy04EHziO/vEbmaoiIwYqIqBX2PL+q0dDYIAzuGoA6vRGLN5+Uuxwil8dgRUTUisalFuzticArKRQKPD05DkDD7cCMvAqZKyJybQxWREStOHVRC8C+R6wAIDE6ADclREAQgFd+Ps4nBIlkxGBFRNSCsmodiqvqANjf4qAt+eeEPnBTKfDHqWJsO1kkdzlELovBioioBY3zqyL9PeCjUctcTduig7wwN6krAODVnzNhMHLUikgODFZERC1oeiIwzH6fCDT18Jge8PNQ40RhJVYd4PILRHJgsCIiakHTE4Eh9n8bsFEnL3c8MqYnAOCNzSdQrdPLXBGR62GwIiJqQeOIVc8wxwlWADBnaAy6BHjiYmUdPtqRI3c5RC6HwYqIqAXZDrCGVUs0ahX+ObEPAOD9bdm4WFkrc0VEroXBiojIhLZOjwtlDauYO9KtwEY3J0RgQFQnVOsMeOvXLLnLIXIpDFZERCayixpGq4J93BHg7S5zNZZTKBR45vKiod/uy0VWYaXMFRG5DgYrIiITWYWOeRvwStd1C8T4vmEwGAW8tiFT7nKIXAaDFRGRiVNFjh+sAOCpSX2gUirwW+ZFvPXrSegNRrlLInJ6DFZERCaaRqwccH7VlWJDfPBgciwA4K1fs3Dbh7txvrRa5qqInBuDFRGRicY5Vj0daHHQ1vxjQm+8OWsAfDRq7D9biklv78DaI3lWv44gCCivrkdmQQW2ZF7El3vO4vPUMyjV6qx+LSJ7Zv/7NBAR2VBtvQFnLznG5svmmprYBQOjA/HYt4dw6FwZHv36ELaeuIh/T+knabseg1HAnpxL2JRegOwiLfLKa1BQXotqnaHZa/+z6QQeHBWLu4Z1g4ebyhp/HSK7phC4DbpNVVRUwN/fH+Xl5fDz85O7HCIykVlQgYlv7YCvhxpHn78BCoVC7pKsRm8w4p3fsvDfLadgFIDoQC+8fds1SIwOaLOv0Shg35kS/JSWj5/TCpo2qDYV4OWGcH9PRPp74EJZDTILGp5IDPfzwOPje2L6wCiolM7zb0quw9zPb45YERFd4conAp0pVAGAWqXE/Bt6Y3jPEDz+7WGcK6nG9PdTMbp3KEJ8NQjydkeQjzsCvd0R5K1BoLc7tDo9fk7Lx89p+Sis+DNM+Xu6YWJ8OAZ3C0SkvwciOnki3M8Dnu5/jkoZjQJ+PHwBizefxIWyGjy5Kg0f7cjBU5P6YEyfUKf79yUCGKyIiEQa9wjs6SS3AVtyXbdA/PzYCDzzQxrWH83Hr8cLzern66HGhPhw3JgQgWGxwXBXX32arlKpwLRru2By/wj8b/dZLP39FLIuVuHuz/bjum6B+NfN8egbyZF7ci4MVkREVzjloFvZWMrf0w1L/5KI2UNicOpiJS5pdbhUpUOJVodL2jqUaBv+W28UMLp3KG7sH4ERvYKhUVs+T8rDTYV7RnTHjEFReG9rNlbszMHenBL89aPd2PqPUejk5XiLsBK1hsGKiOgKf45YOf4TgW1RKBRIig1CUmyQTa7n7+mGpyb1wZykGMz9ZC+yLlbhnd9O4bmb+9rk+kS2wOUWiIgu0xuMOF3sGiNWcors5Ilnb2oIU5+nnsHpy8tbEDkDBisiosvOlVSj3iDAw02Jzp085S7HqY3sFYLRvUOgNwp4lVvukBNhsCIiuizr8m3A2BAfKLkkQId75sY4qJQK/JJRiF3ZxXKXQ2QVDFZERJdl5jesudTbCVZcdwQ9Qn0xe0g0AOCl9cdhMHJZRXJ8DFZERJdl5JcDAJcAsKG/j+sFXw81MvIrsOrAebnLIWo3Bisiossy8isAMFjZUqC3Ox4b2xMA8J/NJ1BVp5e5IqL2YbAiIgJQXlOP3JIaAEDfCAYrW7ojKQYxQV4oqqzDB9uy5S6HqF0YrIiIAGReHq3q3MmTC1bamEatwsJJcQCAD7efxoWyGpkrIpKOwYqICH/eBozjaJUsJsSHYUi3QNTpjfjPRi6/QI6LwYqICEBGHudXyUmhUODZm/pCoQB+PJyHw7llcpdEJAmDFRERrpi4zhEr2fTr7I9br+0CAHhxfQYEgcsvkONhsCIil6fTG5FV2LA4aDxHrGT1xITe8HRT4cDZUvySUSh3OUQW65BNmPPz87Fnzx7k5ORAq9XC09MT0dHRGDRoELp169YRlyQikiy7qAo6gxG+GjW6BHArGzmF+XngjqQYfLj9NNYeycMN8eFyl0RkEasGq3Xr1mHRokXYtWtXq0O4AwYMwPz583HHHXdAobDPLSM++eQT3H333c3a586di08//dT2BRFRh2qcXxUX6We3P5dcyaR+4fhw+2lsPVGEOr0BGrVK7pKIzGaVW4FVVVWYPn06UlJSsHPnzqveFz9y5Ajmzp2L0aNHo7DQ/oZ5CwsL8Y9//EPuMojIhji/yr4M6NIJYX4aVNXpsSv7ktzlEFmk3cGqqqoK48aNw6pVqyzqt23bNgwfPhwFBQXtLcGqHn30UZSWlspdBhHZEJ8ItC9KpQI39G24Bbj5mH19RhC1pd3B6q677sKePXtEbdHR0XjrrbeQlpaG0tJSZGRkYPny5YiLixO97tSpU5g2bRr0evvYwmD9+vX47rvv5C6DiGxIEASOWNmhG+LDAAC/ZBRyc2ZyKO0KVuvWrcPKlStFbcnJyTh69Cgee+wx9OvXD506dUJcXBzuueceHDp0CDNmzBC9PjU1Fe+//357yrCKqqoqPPTQQ03H8fHxiIyMlLEiIrKFvPJalNfUQ61UoGeYj9zl0GXXdw+Cr4caxVU6HDrHuwjkOCQHK0EQ8Oyzz4raoqOjsW7dOvj7+7fYR6PR4Ouvv8bAgQNF7S+//DKqq6ullmIVCxcuRG5uLoCGheref/99uLm5yVoTEXW8xtuAPUJ9OEnajriplBjbJxQAsJnLLpADkRys1qxZgyNHjoja3n77bfj6+l61n0qlwvLly0VtBQUFzdpsaffu3Vi2bFnT8d13343hw4fLVg8R2Q7nV9mvCZeXWth0rICLhZLDkBysTOcide3aFVOmTDGrb2JiYrPgYnpL0Vbq6+tx7733wmg0AgBCQkKwaNEiWWohItvLyC8HAMRHtjzSTvIZ2SsE7molzl6qxonCSrnLITKLpGBVX1+Pn3/+WdRm6bpU8+bNEx2npqbi4sWLUsppl9deew3p6elNx4sXL0ZgYKDN6yAieXDiuv3y1qgxsmcwAGDzMd4OJMcgKVilpqaivLxc1DZq1CiLzmH6eqPRiI0bN0opR7ITJ07g5ZdfbjoePXo07rjjDpvWQETyKa+pR25JDQAGK3vVuOzCJi67QA5CUrA6evSo6FilUmHIkCEWnSM2NhZhYWGitrS0NCnlSCIIAu677z7U1dUBaJhYbw9PJxKR7WReHq3q3MkT/l58WMUejY0LhVIBHMurwPlSeR9yIjKHpGCVmZkpOo6Ojoa3t7fF5zFd18r0vB3pww8/xPbt25uOn3rqKfTq1ctm1yci+TXdBuTEdbsV5KPB4K4N0zN4O5AcgaRgdfz4cdFxdHS0pItHRUWJjm0VrPLz8/Hkk082Hffq1QsLFy60ybWJyH40PRHI24B2rXEj5s0ZvB1I9k9SsMrLyxMdmwYkc5kGsgsXLkg6j6Uefvhh0RyxZcuWQaPR2OTaRGQ/OGLlGG7o2zBtZG9OCUq0OpmrIbo6ScGqqqpKdOznJ+2Hkmm/mpqapmUPOsqPP/6I1atXNx3ffvvtGDt2bIdek4jsj05vRFZhw88yjljZt6hAL/SN8INRAH49ztuBZN+sEqw8PT0lXbylfqbntqaKigr87W9/azoOCAjA4sWLO+x6AFBXV4eKigrRFxHJL7uoCjqDEb4eanQJkPYzjGyncbFQzrMieycpWGm1WtGxh4eHpIvbOlg9+eSTotuYixYtQmhoaIddDwBeffVV+Pv7N31JvW1KRNZ15fwqS9bgI3k0bsq8I6sI1Tq9zNUQta5dmzA7kj/++AMffPBB0/GwYcNwzz33dPh1Fy5ciPLy8qavxv0IiUhenF/lWPqE+yI60At1eiO2nyySuxyiVkkKVqZLK9TW1kq6eE1NTbM2Hx/r7y5fV1eHe++9t2mvKTc3N3zwwQc2+S1Vo9HAz89P9EVE8uMTgY5FoVA0TWLfxNuBZMckBSvT8NNSQDKHrYLVK6+8IlrKYcGCBYiPj7f6dYjIMQiCwBErBzShX8M8q9+OF6Le0LEPOhFJZZVgJXVCtmk/T09PKJXWvTuZkZGB1157rem4W7dueO6556x6DSJyLBfKalBeUw83lQI9Q33lLofMdG10AIJ93FFRq8ee0yVyl0PUIkkpJjIyUnQsdd6QaT/T81rDPffcA53uz3VP3n33XclPMRKRc2i8Ddgj1BfuapeZaurwVEoFxsU13g7kYqFkn9RSOvXp0we///570/G5c+ckXdy0n+kWN9aQmpra9N/BwcHYtm0btm3bZlbf0tJS0fGBAwfw1FNPidpmzJiBgQMHtr9QIrKZptuAnF/lcCbEh+Obfbn49XghXryln9zlEDUjKViZBqDc3FxotVqL9ws03cKmT58+UsoxW3FxMRYtWiS5f3p6OtLT00Vtffr0YbAicjBNE9c5v8rhXN89CEoFkF9ei4sVtQj1k7bcD1FHkTQGnpCQIDrW6/XYu3evRefIzs5GQYF4KLd///5SyiEisghHrByXp7sKPUIb5vmmXShv49VEticpWCUlJcHf31/UtnXrVovOYXo7TqlUYuLEiVLKISIyW3lNPc6XNjyRzGDlmPp1bvj8Sb/AnSzI/kgKVm5ubpg8ebKo7YsvvmhaJ8ocn332meg4KSmpQ1ZBFwRB8ldMTIzoXHPnzm32mnnz5lm9ZiLqOMcvj1Z17uQJfy83mashKfpFNgQrjliRPZL8OMzMmTNFxzk5OVi3bp1ZfY8cOYLt27eL2mbMmCG1FCIis3F+leNrHLE6lsdgRfZHcrCaMmVKs7lWjz76aJt7/RkMhmZbyYSHh+Pee+8167oKhUL0NWrUKIvqJiLXxvlVjq9vpB8UlyewF1fVyV0OkYjkYKVQKPDSSy+J2s6ePYuUlJRWFwzV6XSYPXs29u/fL2p/5pln4OXlJbUUIiKzpV++fcQRK8flo1GjW3DDU+jpvB1IdqZdK+PdfPPNuPXWW0VtW7ZsQUJCApYuXYqMjAyUlZXhxIkT+OSTT5CYmIhvv/1W9PqkpCQ88MAD7SmDiMgs+eU1yCyohELRsIo3Oa7+TRPYGazIvkhax+pKK1asQG5urmi5hbNnz+LRRx9ts29sbCxWrVoFtbrdZRARtWnz5c17B0YHIMRXI3M11B79Iv2x5nAenwwku9PuvRx8fX3x66+/YurUqRb1GzFiBHbu3ImIiIj2lkBEZJbGbVAmxIfLXAm1V+MEdj4ZSPbGKptk+fr6YvXq1VizZg2GDh161dcmJCTg008/xbZt2xAWFmaNyxMRtalUq8OenIaNexmsHF9854Y5chfKalCq1bXxaiLbseo9uJSUFKSkpCAvLw+7d+/GmTNnoNVq4enpiaioKAwePBjdu3dv1zUsWSurvc6cOWOzaxFRx/r1eCEMRgFxEX6IDuLDMo7Oz8MNXYO8cOZSNdLzyjGiZ4jcJREBsHKwahQZGYlp06Z1xKmJiCTZdHl+1YR4jpQ7i/jO/g3B6kIFgxXZDavcCiQismfaOj12ZBUB4G1AZ8InA8keMVgRkdPbdrIIdXojogO90CfcV+5yyEoat7ZJ5wrsZEcYrIjI6TU+DTixXzgUCoXM1ZC19Ls8gf3spWqU19TLXA1RAwYrInJqOr0Rv2deBMD5Vc6mk5c7ugR4AuC+gWQ/GKyIyKmlnr6Eylo9Qnw1SIziauvOhvOsyN4wWBGRU9uY3nAbcHzfMCiVvA3obPo1BSuuwE72gcGKiJyWwSjgl4yGZRYm8mlAp9SPI1ZkZxisiMhpHTpXiuKqOvh6qHF99yC5y6EO0C+yYQL76WItKms5gZ3kx2BFRE6r8WnAsX1C4a7mjztnFOSjQaS/BwAgI4+3A0l+/ElDRE5JEARs5KbLLiG+8XYggxXZAQYrInJKx/MrkVtSA41aieTe3O7EmTU+GXiM86zIDjBYEZFTarwNOLJXCLzcO2RbVLITjQuFpjFYkR1gsCIip7SJtwFdRuOTgdlFVajW6WWuhlwdgxUROZ2zl7TILKiESqnAuLhQucuhDhbq64FQXw2MAnA8n/OsSF4MVkTkdBpHq4Z0C0QnL3eZqyFb6M+FQslOMFgRkdPZdOzyoqD9eBvQVTQ+Gch5ViQ3BisicioXK2px8FwpAOCGvgxWrqJxoVCuwE5yY7AiIqfywfbTEARgQFQnhF9eOJKcX/8uDSNWWRerUFtvkLkacmUMVkTkNHZkFeHjP3IAAI+N7SFzNWRL4X4eCPJ2h8EoILOgUu5yyIUxWBGRUyjR6rDguyMAgNuvj8aYPmEyV0S2pFAompZd4DwrkhODFRE5PEEQsHD1UVysrENsiDeemdxX7pJIBo0LhXIFdpITgxURObxv9+Vi07FCuKkUePu2RHi6q+QuiWTQnyNWZAcYrIjIoZ0uqsIL6zIAAP+4oXfT7SByPfGRDf/fnyysRJ2eE9hJHgxWROSw6g1GPP7tYdTUG5DUPQj3jugud0kkoy4Bnujk5YZ6g4CTBVVyl0MuisGKiBzW279m4cj5cvh7umHxzAFQKhVyl0QyUigU6BfJ24EkL275TkRWV1tvwPaTRdibU4Jwfw/ER/ojvrMf/DzcrHaNPacv4d2tpwAAr0ztj8hOnlY7Nzmu+Eg//HGqGJkF3NqG5MFgRURWUV5Tj98zC7EpvRDbThahpoVFGmOCvNAv0h/9OvujX2c/xEf6I9Db8r38ymvqMf+7IxAEYPrALrgxIcIafwVyAn0ifAEAmflcy4rkwWBFRJKVanX4KS0fm44VIDX7EvRGoenPOnfyRHLvEJRU6ZCeV47zpTU4e6kaZy9V46e0/KbXhfpq0CfCD3Hhvugd7os+4X6IDfWGRt3wZF95TT3OXtIip1iLs5eqceaSFkdyy3ChrAbRgV74V0q8zf/eZL96hzUsuZBZUAFBEKBQ8PYw2RaDFRFJcqZYixkfpKKosq6prVeYDybEh2NCfDjiI/1EH2qlWh2O5VUg7UI50vPKkX6hHGcvVeNiZR0uVhZh+8mipteqlQpEB3qhtFqH0ur6Fq+vUSvx1m3XwEfDH2P0p9hQb6iVClTU6pFfXstbxGRz/IlERBYrKK/F7R/vQVFlHWKCvHDb4GhMiA9D9xCfVvsEeLtjeM9gDO8Z3NRWVafHiYJKnCioRGZBBTILKpGZX4GKWj1OF2ubXhfiq0G3IG/EBHmha7A3ugZ5IzG6Ez80qRmNWoXYEB+cKGx4T/E9QrbGYEVEFinV6nDHx3twvrQGXYO8sPKBoQjx1Ug6l49GjYExARgYE9DUJggC8strcbpIi0Bvd8QEecGbo1Jkgd7hvpeDVSW3NiKb408rIjJbVZ0e8z7dh6yLVQj388AXdw+RHKpao1AoENnJkyMNJFmfCF+sPcIJ7CQPrmNFRGaprTfgvs/340huGQK83PDF3dchKtBL7rKImokL/3MCO5GtMVgRUZv0BiMe/foQdmVfgre7Cp/eeR16hvnKXRZRixqXXDhdpOXWNmRzDFZEdFVGo4CnVqdhc0Yh3NVKLJ87CAOiOsldFlGrwv084Oehht4oIPuitu0ORFbEYEVErRIEAS/9dBzfHzgPlVKB//4lEUNjg9vuSCQjhUKBPhG8HUjyYLAiolatOngBn+zMAQC8fmsCbogPl7kiIvPEhTfcDjxRwAnsZFsMVkTUIqNRwLItDXvx/X1cT9w6sIvMFRGZr/flCezHGazIxhisiKhFvx4vxOliLXw91LhnRHe5yyGyyJ97BvJWINkWgxURtWj5jtMAgNuvj+G2MeRwel9+avViZR1KtDqZqyFXwmBFRM0cPFeKfWdK4aZSYN7QrnKXQ2Qxb40a0ZfXWeMEdrIlBisiamb59obRqluu6YwwPw+ZqyGSpk944+1AzrMi22GwIiKRM8VabDxWAAC4dyTnVpHjalxygU8Gki0xWBGRyMd/5EAQgNG9Q9CLq6uTA2saseKtQLIhBisialKi1WHlgVwAHK0ix9cYrE4UVsJgFGSuhlwFgxURNfki9Sxq643o39kfSd2D5C6HqF1igrzh4aZEbb0R50qq5S6HXASDFREBAGrrDfg89QyAhtEqhUIhb0FE7aRSKppuZ3M9K7IVBisiAgCsOngel7Q6dO7kicn9uHUNOYfG24FcgZ1shcGKiGA0CvhoR8OegHcP7wa1ij8ayDn0CW98MpAjVmQb/OlJRPjleCFyirXw81Bj1uAoucshspo/nwzkiBXZBoMVETUtCHr79THw5vY15ER6Xw5WZy9VQ1unl7kacgUMVkQu7sDZUuw/Wwp3lZLb15DTCfLRINRXAwA4WchRK+p4DFZELq5p+5rESIRy+xpyQo0rsPN2INkCgxWRCyuuqsPmjMvb14zggqDknP7cM5AT2KnjMVgRubDNxwphFICELv7oye1ryElxAjvZEoMVkQvbkJ4PAJjIdavIiTUuuZBZUAlB4NY21LEYrIhcVHl1PVKzLwEAJvWLkLkaoo4TG+oNlVKB8pp6FFTUyl0OOTkGKyIX9cvxQuiNAvqE+6JbsLfc5RB1GI1ahdiQhvc4bwdSR2OwInJRG3kbkFxI0+3AfAYr6lgMVkQuqLK2HtuzigHwNiC5ht5NE9j5ZCB1LAYrIhf0e+ZF6PRGdA/xRq8wH7nLIepwcRENweoEbwVSB2OwInJBG9Mb1q6a1C8cCoVC5mqIOl7jrcBTF6ug0xtlroacGYMVkYup0Rmw9UQRAN4GJNcR4e8BXw819EYB2UVVcpdDTozBisjFbDt5ETX1BnQJ8ER8pJ/c5RDZhEKhQNzlUSveDqSOxGBF5GI28DYguag+l+dZHecEdupADFZELqROb8Bvxy8CACbyNiC5mKYnA7nkAnUgBisiF/JHVjGq6vQI9/NAYlQnucshsqnGCezH8yu4tQ11GAYrIhfSeBtwYr9wKJW8DUiuJS7CFxq1Ehcr63CikKNW1DEYrIhcRL3BiF8yCgFwtXVyTV7uaozsFQIA+DmtQOZqyFkxWBG5iN2nL6G8ph7BPu4Y3DVQ7nKIZDG5f8MvFRvS8mWuhJwVgxWRi2i8DTi+bzhUvA1ILmpsXBjcVApkXaxCFm8HUgdgsCJyAQajgM3H/lxmgchV+Xm4YUTPhtuBjb9sEFkTgxWRC9h3pgTFVTr4e7ohKTZI7nKIZNX4ywWDFXUEBisiF7Cx6TZgGNxU/LYn1za+bxjUSgWO51cgp1grdznkZPgTlsjJGY2CaNNlIlfXycu9aeR2QzonsZN1MVgRObnD58tQUFELH40aw3sGy10OkV2Y3L9h54ENXHaBrIzBisjJrT/S8Bv5mD6h0KhVMldDZB9u6BsGpQJIu1CO3JJqucshJ8JgReTE6g1GrD1yAQBwS2KkzNUQ2Y8gHw2u787bgWR9DFZETmxHVhGKq3QI9nFvesSciBpMarwdyKcDyYoYrIic2KoDDaNVKQM682lAIhMT4sOgUACHzpUhr6xG7nLISag74qT5+fnYs2cPcnJyoNVq4enpiejoaAwaNAjdunXriEtazGAw4NSpU0hPT0dhYSHKysqg0WgQGBiIqKgoDBkyBL6+vnKXSSRZeXU9fjnesDfgtGs7y1wNkf0J9fXA4JhA7D1Tgo3pBbhruH18PpFjs2qwWrduHRYtWoRdu3ZBEIQWXzNgwADMnz8fd9xxBxQK226rkZGRgfXr1+P333/Hjh07UF3d+oRFpVKJQYMG4aGHHsJtt90GjUZjw0qJ2m99Wh50eiP6hPsiPtJP7nKI7NKk/uHYe6YEG9LzGazIKqxyb6CqqgrTp09HSkoKdu7c2WqoAoAjR45g7ty5GD16NAoLC61x+TZ9+eWX6N+/P+Lj4/Hkk09i06ZNVw1VAGA0GrF3717MmzcP8fHxSE1NtUmtRNay+mDDbcBp13a2+S8xRI5i4uW13fafLUVhRa3M1ZAzaHewqqqqwrhx47Bq1SqL+m3btg3Dhw9HQUHHTxpct24d0tPTJffPzs7GiBEj8OWXX1qxKqKOc6ZYiwNnS6FUALdcw9uARK2J8PfEtdGdIAjApmOcxE7t1+5gddddd2HPnj2itujoaLz11ltIS0tDaWkpMjIysHz5csTFxYled+rUKUybNg16vb69ZVhsyJAhWLhwITZu3IjMzEyUlJSgqKgIBw4cwOuvv47o6GjR6w0GA+bNm4fNmzfbvFYiS60+eB4AMKJnCEL9PGSuhsi+NS4W+nMal10gKxDaYe3atQIA0VdycrJQVlbW4utra2uFGTNmNOuzdOnS9pTRplmzZgkAhMDAQOGJJ54QTpw40Waf2tpa4b777mtWa7du3YSamhrJtZSXlwsAhPLycsnnILoag8EoDHvtNyHmyfXCj4fOy10Okd3LLdEKMU+uF7o9tV4oqqyVuxyyU+Z+fksesRIEAc8++6yoLTo6GuvWrYO/v3+LfTQaDb7++msMHDhQ1P7yyy+3OeepPXx8fPDMM8/g9OnTeP3119GrV682+2g0GnzwwQeYM2eOqD0nJwcrVqzoqFKJ2m3fmRKcL62Bj0aNG/pyb0CitnQJ8EJCF38YBWDzMdvM/SXnJTlYrVmzBkeOHBG1vf32220uUaBSqbB8+XJRW0FBQbM2a/rggw/w0ksvtRr4ruatt95Cp06dRG0//vijdQoj6gCrLt8GvLF/BDzduYUNkTkm9WtcLJS3A6l9JAer7777TnTctWtXTJkyxay+iYmJGD58uKht5cqVUktpk0ol/cMlICAAKSkporadO3e2tySiDlGjM+Dny5vKcu0qIvNNuvx04K7sSyjV6mSuhhyZpGBVX1+Pn3/+WdRm6bpU8+bNEx2npqbi4sWLUsrpcAMGDBAda7VaVFZWylQNUes2ZxSgqk6PLgGeGNw1UO5yiBxG12Bv9I3wg8Eo4L9bTsFobH3ZIKKrkRSsUlNTUV5eLmobNWqURecwfb3RaMTGjRullNPhPD09m7VptVoZKiG6ulVNa1d1gVLJtauILDF3aAwA4OM/cvDQlwehrbP9E+vk+CQFq6NHj4qOVSoVhgwZYtE5YmNjERYWJmpLS0uTUk6HO3funOhYoVAgODhYpmqIWlZYUYs/sooAANMSeRuQyFKzBkfj9ekJcFcpsfFYAW59bxdySzruwSpyTpKCVWZmpug4Ojoa3t7eFp/HdF0r0/Pai23btomOY2JioFZ3yDaLRJL9eOgCjAIwMCYAXYMt/34kImDmoCh8fd/1CPbRILOgEin//QO7sovlLosciKRgdfz4cdGx6WKa5oqKihId22OwOnnyZLPtbCZNmiRTNUQtEwSh6WnAW6/tInM1RI5tYEwA1j0yDAld/FFaXY87Pt6Lz3aduep2bQajgIsVtaitN9iwUrJHkoZd8vLyRMemAclcpoHswoULks7TkZ5//vlmbbNmzZKhEqLWHcurwMnCKrirlbjx8irSRCRdhL8nvrs/CQtXp+GHQxfw/NpjOJ5fgadvjMP5khqcKqrCqYtVyC6qQvbFKpwu1kKnNwIAvN1VCPLRIMjHHUHeGgT7uCPYR4NrojphbFwo9+50cpKCVVVVlejYz89P0sVN+9XU1MBoNEKptMre0O22fv16fPPNN6K2kSNHIjk5WaaKiFrWOFo1Pi4M/l5uMldD5Bw83FRYMnMA4iJ88dqGTHyzLxff7Mtts59WZ4C2pBrnWpifNaxHEF5I6YceoT4dUTLZAasEq5aemjNHS/2qqqokBzVrunDhAu68805Rm5ubG5YuXWrReerq6lBXV9d0XFFRYZX6iBpV1emx5nDDKPKtAzlpnciaFAoF7hsZi15hvnjsm8Mor6lHJy839AjxQWyID3qE+iA21Bs9QnzROcAT1To9LlXpUFxVh+IqHS5p63CpSoe8shr8cOgCdp66hElvb8fdw7vj0bE94OXO+brORtL/o6ZLDXh4SNvk1V6DVW1tLaZOnYriYvGExRdeeAEJCQkWnevVV1/FCy+8YM3yiETe+uUkSrQ6RAd6YUTPELnLIXJKo3qHYt8z41BZW49Ab/dWb+f5erjB18OtxQdIHhrVAy+sO4bfMi/i/W3ZWHv4Ap69qS8m9gvn7UEnYh/33OyI0WjEHXfcgX379onaJ02ahKeeesri8y1cuBDl5eVNX7m5bQ8jE5nreH4FVuw6AwB4YUo83FT8libqKO5qJYJ8NJJDUHSQFz6eNxgfzRmELgGeyCuvxYNfHsTcFfuQU8y1EZ2FpJ/Cpksr1NbWSrp4TU1NszYfH3nvOz/yyCP4/vvvRW0DBgzAN998I+mbSaPRwM/PT/RFZA1Go4BnfkiDwShgUr9wjO4dKndJRGSGcX3D8Ov8ZDw6pgfcVUpsP1mECW9ux/cHzstdGlmBpGBlGn5aCkjmsLdg9dxzz2HZsmWittjYWGzcuJGBiOzOt/tzcfBcGbzdVXju5r5yl0NEFvBwU2H+Db2x+fGRSO4VAp3BiCe+P4I1h+3v6XiyjFWCldQJ2ab9PD09ZXsicMmSJXjxxRdFbZGRkfjll18QHh4uS01ErblUVYfXNjSs+/b4+F6I8Jf2AAkRyatrsDc+vXMwZg+JhiAA8787go3p+XKXRe0gKcVERkaKjqXOGzLtZ3peW1m+fDkWLFggagsODsYvv/yCbt26yVIT0dW8uiET5TX16BPui3lDu8pdDhG1g0KhwItT+mH6wC4wGAU88vUhbMm8KHdZJJGkYNWnTx/RseleeuYy7We6xY0tfP3113jggQdEbX5+fti4cSP69uXtFbI/e3NKmuZivDy1H9ScsE7k8JRKBRbdmoCbB0Si3iDg/v8dwB9Z3ErHEUn6iWwagHJzc5stwWAO0y1sTANbR1uzZg3mzJkDo9HY1Obl5YX169dj4MCBNq2FyBz1BiP+78eGzcr/cl0UBsYEylwREVmLSqnAkpkDMCE+DDq9Efd8vg97Tl+SuyyykKRgZbqWk16vx969ey06R3Z2NgoKCkRt/fv3l1KOJJs3b8asWbOg1+ub2tzd3fHDDz9gxIgRNquDyBIf/5GDk4VVCPR2x5MTbfuLCBF1PDeVEu/8JRGjeoegtt6Iuz7dh4PnSuUuiywgKVglJSXB399f1LZ161aLzrFt2zZxIUolJk6cKKUci/3xxx+YOnWqaEV0tVqNb775BjfccINNaiCy1PnSarz9axYAYOGkPujk5S5zRUTUETRqFd6/fSCGxgZBqzNg7id7kX6hXO6yyEySgpWbmxsmT54savviiy+uuvO3qc8++0x0nJSUhNDQjl+H58CBA7jxxhtRXf3nHk5KpRKffvoppk6d2uHXJ5LqhXUZqKk34LqugZg+sIvc5RBRB/JwU+GjuYMwuGsAKmv1mPPJXhRWSFszkmxL8qzXmTNnio5zcnKwbt06s/oeOXIE27dvF7XNmDFDailmy8jIwMSJE5st8/Dee+9h9uzZHX59IimMRgHLt5/GLxmFUCsVeGlqP25/QeQCvNzV+GTeYPSN8EOJVocF3x2B0Wj+AAbJQ3KwmjJlSrO5Vo8++mizDZpNGQwG3HPPPaK28PBw3HvvvWZdV6FQiL5GjRplVr/Tp09j/Pjxzfb/W7JkCe677z6zzkFkaycLKzH9/V14+efjAID7k7ujV5ivzFURka34erhh6V8T4eGmxB+nivHJzhy5S6I2SA5WCoUCL730kqjt7NmzSElJaXXBUJ1Oh9mzZ2P//v2i9meeeQZeXl5SS2lTXl4exo0bh7y8PFH7iy++iMcff7zDrkskVZ3egCWbT+DGd3bg4Lky+GjU+PeUeCwY31vu0ojIxmJDfPDcTfEAgNc3nsCxPM63smcKwZKJUS2YPn06Vq1aJWqLiYnBggULMHbsWERGRqKwsBA7d+7E4sWLkZGRIXptUlIStm/fDrVabV7BJrdAkpOT25w4P2zYMOzatUvUFhERgTlz5ph1zZbMmDFD0pIMFRUV8Pf3R3l5ObfJoRbtO1OCp1YdRXZRwxIm4+JC8eIt/bi6OpELEwQB931xAL9kFKJHqA/WPzIcHm4quctyKeZ+frc7WFVWVmLcuHEWL7cANOzDt2PHDkRERJjdR0qw6tq1K86ePWtxfVezYsUKzJs3z+J+DFbUEqNRQEm1Dkt+OYmv9jQsnBvso8ELKfGY3D+cc6qICCVaHSa8tR1FlXWYmxSDF6b0k7skl2Lu57d5w0RX4evri19//RVz587FDz/8YHa/ESNGYOXKlQgLC2tvCUR2o0ZnwLmSapRW61BWrUNpdf3l/65HqVaHspp6VNXqodXpUVWrR1WdHto6PbQ6g+g8tw2OwsJJcfD3cpPpb0JE9ibQ2x2LZwzAnE/24rPUsxjVOxSj+3T80/RkmXYHK6AhXK1evRpr167FokWLmt12u1JCQgLmz5+POXPm8Ldwchp6gxFf7jmHNzafQGWtvu0OregZ6oN/T+mHpNggK1ZHRM5iZK8Q3DWsGz7ZmYMnvj+CDY+NRIivRu6y6ArtvhXYkry8POzevRtnzpyBVquFp6cnoqKiMHjwYHTv3t3al3MovBXofA6cLcGzPx5DRn7DQxt+HmoE+2jQycsNgd7u6OTljgAvN3TyckcnLzf4erjBR6OCt7sa3ho1fDRq+Hg0/K9GreQvHER0VbX1Btzy7k5kFlRidO8QfDJvMH9u2IDN5liRZRisnEdxVR1e25DZtCGyv6cb/jGhN/56XTRUSv6QI6KOc6KgEjf/9w/o9Eb8e0o85iR1lbskp2fu57fk5RaIXJXBKODz1DMY88bWplA1a1AUfl+QjDuuj2GoIqIO1zvcF09Patgv9OWfjuNkYaXMFVEjq8yxInIVpy5W4rFvDuNYXsNtv/hIP7x4Sz9cGx0gc2VE5GrmDu2KLSeKsO1kERauTsP3DyTxlqAd4IgVkZkuVtRizsd7cSyvAn4earx4Sz+sfXg4QxURyUKhUGDRrQnwclfhwNlS/HDogtwlERisiMxSozPgns/3I6+8Ft1DvPHbglG87UdEsgv398AjY3oCAF75OROVtfUyV0QMVkRtMBoFzP/uMI6eL0eAlxtWzBvMx5uJyG7cNbwrugd7o7iqDm//miV3OS6PwYqoDf/ZfAIb0gvgrlLigzsGISbIW+6SiIiaaNQqPJ/SsJfgil1nOJFdZgxWRFfx3f5cvLc1GwCwaHp/XNctUOaKiIiaS+4Vghv6hsFgFPCvtcfAlZTkw2BF1IrU7Et45oc0AMAjY3pgamIXmSsiImrdszf1hUatxK7sS/g5rUDuclwWgxVRC04XVeGB/x1AvUHATQkReHxcL7lLIiK6qqhALzyQHAsAeOmnDFTrpG+vRdIxWBGZKNXqcPdn+1FeU4/E6E54Y8YAKPn0HxE5gAdHxaJLgCfyy2vx7pZTcpfjkhisiK5gMAp48MsDyCnWonMnT3x4xyB4uKnkLouIyCwebio8e1NfAMDy7TnIKdbKXJHrYbAiusL3B3Kx+3QJfDRqfMJlFYjIAd3QNwwje4VAZzDihXWcyG5rDFZEl1XV6fGfTScBAH8f1xO9w31lroiIyHIKhQLP39wXbioFtp4owm/HL8pdkkthsCK67L2tp1BcVYeuQV7cKZ6IHFpsiA/uHt4dAPDC+mOo0xtkrsh1MFgRAThfWo3lO3IAAAsnx8FdzW8NInJsj4zpgVBfDXJLavDd/vNyl+My+OlBBOD1jSeg0xtxffdA3NA3TO5yiIjazVujxt9G9wAALNtyiqNWNsJgRS7v4LlSrD2SB4UC+L8b+0Kh4NIKROQcZg2OQrifB/LLa/Htvly5y3EJDFbk0gRBwIvrMwAA06/tgn6d/WWuiIjIejzcVPjb6IZFQ9/dcgq19Ry16mgMVuTS1h3Nx6FzZfByV+GJCb3lLoeIyOpmDo5CpL8HCivq8M3ec3KX4/QYrMhl1dYbsGhDJgDgweRYhPp5yFwREZH1adQqPHR5rtW7W7M5atXBGKzIZX38Rw4ulNUgwt8D94zoLnc5REQdZuagKHTu5Imiyjp8uYejVh2JwYpc0sXKWiy7vI/WkxP7wNOd29YQkfNyVyubnhB8b2s2anQcteooDFbkkpZsPgmtzoABUZ2QMiBS7nKIiDrc9IFd0LmTJ4qr6vDlnrNyl+O0GKzI5WTkVeDb/Q2PHT97YxyUSi6vQETOz12txCNjGkat3t+WjWqdXuaKnBODFbmcJb+cgCAANyZEYFDXQLnLISKymVsHdkFUoCeKq3T4326OWnUEBityKScKKvHr8YtQKIAF43vJXQ4RkU25qZR4ZExPAMAH205DW8dRK2tjsCKX8v62bADApH7h6B7iI3M1RES2Ny2xM2KCvHBJq8PnqRy1sjYGK3IZuSXVWHskDwDwYHIPmashIpKH+opRqw+3Z6OKo1ZWxWBFLuOjHadhMAoY0TMY/btw6xoicl23XBOJbsHeKK2ux2e7zshdjlNhsCKXUFxVh28ub0D6YHKszNUQEcmrYdSqYeT+4z9yONfKihisyCV8tusM6vRGDOjij6TYILnLISKSXcqASMQEeaFEq+O6VlbEYEVOr7L2z6HuB0fFQqHgulVERGqVEn8b1TBq9eH2HO4haCUMVuT0vt57DhW1enQP8cYNfcPlLoeIyG5MvbZz02rsX+/lHoLWwGBFTq1Ob8BHO3IAAA8kx3KVdSKiK7iplHhodMO80/e3ZXPUygoYrMip/XDwAi5W1iHczwO3XNNZ7nKIiOzO9IFdEOHvgcKKOqw8cF7uchwegxU5LYNRwAfbTwMA7hnRDe5qvt2JiExp1Co8cPlp6fe3ZkOnN8pckWPjJw05rY3pBcgp1sLf0w1/uS5a7nKIiOzWrMFRCPHV4EJZDVYf5KhVezBYkVMSBAHvbTsFAJg7tCu8NWqZKyIisl8ebircP7I7AODdradQb+ColVQMVuSU/jhVjPQLFfB0U2He0K5yl0NEZPdmD4lBkLc7cktqsOZwntzlOCwGK3JKy7Y0bLZ823VRCPR2l7kaIiL75+muwr2XR62WbTkFg1GQuSLHxGBFTufQuVKknr4EtVKBe0Z0l7scIiKHcfv1Mejk5YbTxVqsP8pRKykYrMjpLNvaMFo15ZqGhe+IiMg8Pho17hneDQDw399PwchRK4sxWJFTOVFQiV8yCqFQNGxfQ0RElpkztCv8PNTIuliFjccK5C7H4TBYkVN5f1vDaNXE+HD0CPWRuRoiIsfj5+GGO4c1jFq981sWR60sxGBFTiO3pBprjzTMCXjo8saiRERkubuGdYOPRo3MgkqsOXJB7nIcCoMVOY0PtmfDYBQwomcw+nfxl7scIiKH5e/lhr+NbvgF9bUNmdDW6WWuyHEwWJFTuFhRi+/2N6wW3PjDgIiIpLtreFfEBHmhsKIO711+KIjaxmBFTuHjP3Kg0xsxMCYAQ7oFyl0OEZHD06hVeGZyHADgwx2nkVtSLXNFjoHBihxeeXU9/rf7LADgoVGxUCgUMldEROQcxvcNw/AewdDpjXjl5+Nyl+MQGKzI4X2WegZanQF9wn0xpk+o3OUQETkNhUKBZ2/qC6UC2JBegNTsS3KXZPcYrMihVev0WLEzBwDw0OgeHK0iIrKy3uG+uP36GADAC+uOcaubNjBYkUP7em8uSqvrERPkhcn9wuUuh4jIKT0+rhf8Pd2QWVCJb/adk7scu8ZgRQ6rTm/A8u2nAQAPJMdCreLbmYioIwR4u2P++F4AgDc2nUB5db3MFdkvfhKRw/rx0AUUVNQizE+Dadd2lrscIiKnNntINHqF+aC0uh5v/5Yldzl2i8GKHJLBKOD9bQ2jVfeO6A6NWiVzRUREzk2tUuLZm/oCAD5PPYNTF6tkrsg+MViRQ9qQno+cYi06ebnhL9dFy10OEZFLGNEzBOPiwqA3Cnjppwy5y7FLDFbkcPQGI9785SQAYN7QrvDWqGWuiIjIdfzfjXFwUymw9UQRfs8slLscu8NgRQ7nu/3nkV2kRYCXG+4a3k3ucoiIXErXYO+mn71Pr05HWbVO5orsC4MVOZRqnR5v/towWvXo2J7w83CTuSIiItfz2Nie6B7sjYKKWixcnQZB4NpWjRisyKF8vCMHRZV1iA70wuwhMXKXQ0Tkkrzc1Xj7tkSolQpsSC/AygPn5S7JbjBYkcMorqrD+9sadlj/x4TecFfz7UtEJJf+Xfyx4IbeAIB/rT2GM8VamSuyD/xkIoex9LcsaHUGJHTxx039I+Quh4jI5d03sjuu7x6Iap0Bj317GPUGo9wlyY7BihxCTrEWX+5p2EbhqUl9oFRyT0AiIrmplAosmXkN/DzUOJJbhne4cCiDFTmGNzadgN4oYFTvEAyNDZa7HCIiuiyykydenZYAAHh3yynszSmRuSJ5MViR3Tt0rhQ/peVDoWgYrSIiIvtyY0IEpg/sAqMAPP7tYZTXuO5eggxWZNcEQcCrGzIBALde2wV9wv1kroiIiFryr5R4RAd64UJZDZ5bky53ObJhsCK79nvmRezNKYFGrWzaWZ2IiOyPj0aNN2ddA5VSgTWH8/DjoQtylyQLBiuyW3qDEa9dHq26c1g3RHbylLkiIiK6moExAXh0TE8AwP/9mI6ThZUyV2R7DFZkt1YdPI+si1Xo5OWGB0fFyl0OERGZ4W+jY3Fdt0BU1ekx75O9KKyolbskm2KwIrukrdNjyeWNlh8e3QP+nty6hojIEahVSnx4x0DEhngjr7wW81bsQ2Wt60xmZ7Aiu/TSTxkorKhDVKAn7kji1jVERI6kk5c7Pr3zOgT7aHA8vwIP/u8gdHrXWDyUwYrszm/HC/H13lwAwKJbE6BRq2SuiIiILBUV6IUV8wbDy12FP04V46nVR11is2YGK7Irl6rq8OSqNADA3cO7cTFQIiIH1r+LP96dfS1USgVWH7zQNMXDmTFYkd0QBAHP/JCO4qo69Az1wRMTestdEhERtdPo3qF4ZWo/AMDS30/hq8vbkzkrBiuyG6sPXsDGYwVQKxV4c9Y18HDjLUAiImcwa3A0HhvbuAxDGn7PLJS5oo7DYEV24UJZDf619hgA4O/jeqJfZ3+ZKyIiImv6+7iemHF525u/fXkIB8+Vyl1Sh2CwItkZjQL+8d0RVNbpkRjdCQ8kc80qIiJno1Ao8Mq0/hjZKwQ19Qb85cPdWHPY+VZnZ7Ai2X2yMweppy/B002FN2deA7WKb0siImfkplJi2exrMaZPKOr0Rjz2zWG88vNxGIzO87QgP8FIVicLK/H6phMAgGdujEPXYG+ZKyIioo7ko1Fj+ZxB+NvohrsTH24/jTs/3YfyaudYRJTBimSj0xvx+LeHodMbMap3CGYPiZa7JCIisgGVUoEnJvTBf/+aCE83FbafLMKUd/9AlhPsLajuiJPm5+djz549yMnJgVarhaenJ6KjozFo0CB069atIy7ZLsePH8fBgweRn5+Puro6+Pr6IjY2FklJSQgMDJS7PKckCAJe3XAcx/Iq0MnLDa/fmgCFQiF3WUREZEM3JUSiW7A37vv8AM5cqsYt7+7EW7clYnzfMLlLk0whWHEZ1HXr1mHRokXYtWtXq6urDhgwAPPnz8cdd9wh6wdpfX093nvvPbzzzjvIzs5u8TUqlQo33HADnn76aQwfPtwq162oqIC/vz/Ky8vh5+dnlXM6ord/zcKbvzYsFLds9rWY3D9C5oqIiEgul6rq8LevDmL36RIADU8Q/m10D7jZ0Zxbcz+/rRKsqqqqMG/ePKxatcrsPsnJyfj2228RFmb7VJqVlYVp06YhPT3d7D4PP/wwlixZAje39m0GzGAFLN9+Gi//fBwA8OxNfXH3cPsbxSQiItuqNxjx8k/H8emuMwCA7sHeeHpyHMbGhdrFHQ2bBauqqiqMGzcOe/bssbhvjx49sGPHDoSHh7enBIscP34cI0eORHFxscV9p0yZgu+//x5qtfQ7qK4erL7YfRbP/tgQaP9xQy88PKanzBUREZE9+eHQebz803EUV+kAAMN6BOH/buyLuAh5PzPN/fxu9xjbXXfd1SxURUdH46233kJaWhpKS0uRkZGB5cuXIy4uTvS6U6dOYdq0adDr9e0twywVFRW4+eabm4WqIUOG4JtvvkFWVhYuXbqEQ4cO4fnnn0dQUJDodWvWrMHTTz9tk1qd0aoD55tC1YOjYvG30T1kroiIiOzN1MQu2PKPUXggORbuKiV2nrqEG9/ZgYWrj6Kosk7u8trUrhGrdevWISUlRdSWnJyMNWvWwN+/+crZdXV1uOOOO7By5UpR+9KlS/Hwww9LLcNsCxYswJIlS0Rtjz/+OBYvXtziMOP58+cxceJEHDt2rKlNoVDg8OHDSEhIkFSDq45Y/ZyWj4e/OgijAMwb2hXP39zXLoZ2iYjIfuWWVOO1DZn4KS0fQMNSDQ+NjsVdw7rZfNuzDr8VKAgCEhMTceTIkaa26OhopKenw9fXt9V+BoMBQ4YMwYEDB5rawsPDkZ2dDS8vLymlmOXChQvo0aMHamtrm9puueUW/PDDD1ftl5ubi/j4eFRW/vkIaEpKCtasWSOpDlcMVr9nFuK+zw9AbxQwc1AXvDYtAUolQxUREZln35kSvLg+A0fPlwMA/D3dMO3azpg9JBo9QlvPHNbU4bcC16xZIwpVAPD2229fNVQBDU/aLV++XNRWUFDQrM3a/vOf/4hClaenJ5YtW9Zmv6ioKPz73/8Wta1du7bZ351atutUMR7430HojQJuHhCJVxmqiIjIQoO7BuLHh4ZhycwB6BLgifKaeqzYeQbjlmzHzPdT8eOhC6itN8hdJoB2BKvvvvtOdNy1a1dMmTLFrL6JiYnNli8wvT1oTYIgNDv/jBkzEBFh3iP+d911F7y9xSuCd2S9zsBoFPDZrjO467N90OmNGBcXhiUzB0DFUEVERBIolQpMu7YLtj0xGp/eORg39A2DSqnA3jMl+Pu3h5H06m94aX0Gsouq5K1TSqf6+nr8/PPPojZL16WaN2+e6Dg1NRUXL16UUk6b9u/fj7y8PFHb3Llzze7v5+eHqVOnitqk3gp0BTnFWtz24W48v/YYauuNGN07BP/9a6JdrUdCRESOSaVUYFTvUHw4ZxB2PjkG88f3QqS/B0qr6/HRHzkYu3gb1h3Ja/tEHUTSJ11qairKy8tFbaNGjbLoHKavNxqN2Lhxo5Ry2rRhwwbRsbu7O4YOHWrROUzrTU9Px7lz59pbmlMxGAV8tOM0Jr29HXvPlMDLXYV/T4nHx3MH23ySIREROb9wfw88OrYndjw5Bp/MG4RxcaHwcldhRM9g2WqStCDT0aNHRccqlQpDhgyx6ByxsbEICwtDYWFhU1taWpqUctpkWu+1114LDw8Pi84xbNiwZm1paWmIjub+dgBw6mIV/vn9ERw8VwagYd2R16YlICqw4x5IICIiAhpGscb0CcOYPmGoqK2Hn0f7FvNuD0kjVpmZmaLj6OjoZnOQzGG6rpXpea3F9Lym1zVHz549oVKJR106ql5HUm8w4r2t2Zj8zg4cPFcGH40ar0ztj//dPYShioiIbE7OUAVIHLE6fvy46FjqqE1UVJTouCOCisFgQFZWlqhNSr0qlQqRkZHIzc1tanPVYHWpqg7bThbh98yL2H6yCBW1DQu8JvcKwavT+iOyk6fMFRIREclDUrAynQhuGpDMZRpwLly4IOk8V1NcXAydTidqa0+9VwarjqjXHgmCgGN5Ffg98yK2nLiIw7lluHL1syBvdzw1qQ+mD+zCRT+JiMilSQpWVVXiRxmlLnRp2q+mpgZGoxFKpfWeHjOttaXrmsu0X0vnlsvbv2bhbIkWSoUCKoUCSmXDKvFKBaBSKFoMPFeuDas3CtDW6aHVGRr+t06Pqjo9qnUGVNTUQ6sTrw8SF+GHMX1CMKZPKK6JCuAyCkRERLBSsPL0lHbrp6V+VVVVVl2RvKXwY616zQlWdXV1qKv7c2+jiooKSdduy9aTF3Ho8sTxjuDlrsKwHsEY0ycUo3uHItzfssn/RERErkBSsNJqtaJjS5+wa2SLYGVaK2C9es0JVq+++ipeeOEFSdezxLyhXTGpXy0MRsAoCBAEAUahYQkEQRBgEAQo8OeoUuMAVmOLUqmAt7sa3ho1vDWqpv/2uXzcOcATGjWXTCAiIroaScGKzLdw4ULMnz+/6biiokLyHK+rmXJNZ6ufk4iIiCwjKVh5e3ujrKys6fjKPfgsUVNT06zNx8dH0rla09IyENaq15xaNRoNNBqNpOsRERGRY5E0S9w0ULQUkMxhi2DV0vmsVa+1ayUiIiLHZpVgJXVCtmk/T09Pqz4RCLQcfqxVL4MVERERXUlSiomMjBQdX7m2kyVM+5me1xqCg4Ph7u5+1euayxb1EhERkeOSFKz69OkjOpa6GbFpPylbzbRFpVKhR48eV72uOQwGQ7OFUTuiXiIiInJckoKVaaDIzc1tcVmDtphuCWMa2KzFtF7TLXnMcerUKej1elFbR9VLREREjklSsEpISBAd6/V67N2716JzZGdno6CgQNTWv39/KeW0ybTegwcPWvxk4B9//NGsraPqJSIiIsckKVglJSXB399f1LZ161aLzrFt2zZxIUolJk6cKKWcNk2aNEl0rNPpkJqaatE5TOvt16+f5M2niYiIyDlJClZubm6YPHmyqO2LL74Q7T3Xls8++0x0nJSUhNDQUCnltGnQoEHNJpqbXv9qKisrsXr1alHblClTrFIbEREROQ/JaxvMnDlTdJyTk4N169aZ1ffIkSPYvn27qG3GjBlSS2mTQqFodv6VK1c2uxXZmhUrVjSbQzZ9+nSr1UdERETOQXKwmjJlSrO5S48++mib++cZDAbcc889orbw8HDce++9Zl1XoVCIvkaNGmVWvyeeeEK0R2B1dTUefvjhNvudP38ezz77rKgtJSUF11xzjVnXJSIiItchOVgpFAq89NJLorazZ88iJSWl1QU4dTodZs+ejf3794van3nmGXh5eUktxSydO3fGQw89JGpbtWoVnnjiiVZvYZ4/fx6TJk0S/X0UCgVefPHFDq2ViIiIHFO7ljm/+eabceutt4ratmzZgoSEBCxduhQZGRkoKyvDiRMn8MknnyAxMRHffvut6PVJSUl44IEH2lOG2Z5//nl0795d1PbGG29g6NChWLlyJU6fPo3S0lIcOXIE//73v5GYmIj09HTR6xcsWNBspI6IiIgIABSCJTPOW1BZWYlx48ZZvNwCAMTGxmLHjh2IiIgwu49CoRAdJycnW/RE4rFjx5CcnIxLly6Z3adRSkoKVq1aBbVa0t7VAIDy8nJ06tQJubm58PPzk3weIiIisp2KigpERUWhrKys2coIIoIVVFRUCFOnThUAmP01YsQIoaCgwOJrmZ4nOTnZ4nNkZmYK8fHxFtX70EMPCTqdzuJrmcrNzbXouvziF7/4xS9+8ct+vnJzc6/6Od/uEasrrV27FosWLcKuXbtafU1CQgLmz5+POXPmNBt9Mkd7R6wa1dfXY9myZXjnnXdw+vTpFl+jVCpxww034Omnn8aIESMsvkZLjEYj8vLy4OvrK+nv35rGJM2RMHIFfL+TK+H73T4IgoDKykpERkZCqWx9JpVVg1WjvLw87N69G2fOnIFWq4WnpyeioqIwePDgZnOc7MGxY8dw6NAh5OXlQafTwdfXF7GxsUhKSkJQUJDc5ZmloqIC/v7+KC8v5zceOT2+38mV8P3uWDokWJHt8RuPXAnf7+RK+H53LO16KpCIiIiI/sRg5SQ0Gg2ef/55aDQauUsh6nB8v5Mr4fvdsfBWIBEREZGVcMSKiIiIyEoYrIiIiIishMGKiIiIyEqk781CdiE/Px979uxBTk5O05ph0dHRGDRoELp16yZ3eURNzp8/j/T0dJw7dw6lpaUAgICAAISFhWHw4MGIjIyUuUIix1dVVYXU1FScPHkSZWVlcHNzQ1hYGBISEnDNNddYdWFqahmDlYNat25d0yr3rT1/MGDAAMyfPx933HEHv5nI5goLC7F27Vr89ttv2LJlCy5evHjV13fv3h1333037rvvPgQHB1t8vVGjRmHbtm1Sy21SWVkJHx+fdp+HXIs1fsYOHDgQ+/fvl9Q3LS0NL730En788UfodLoWXxMZGYn7778fCxYsgLe3d3tKpatp9+Z3ZFOVlZXCrbfeatG+RsnJyZL2ZSSSYt++fcLo0aMFpVIpaR8uX19f4eOPP7b4usnJyVbZB6yysrID/lXI2VnjvTdw4ECLr2s0GoUXX3xRUKvVZl8nOjpa2LNnTwf8K5AgCALnWDmQqqoqjBs3DqtWrbKo37Zt2zB8+HAUFBR0UGVEf9q/fz+2bNkCo9EoqX9lZSXuvvtuPPjgg1aujMj5PPzww3j22Weh1+vN7nPu3DmMGjUK27dv78DKXBdvBTqQu+66C3v27BG1RUdHY/78+Rg7diy6dOmC/Px87Ny5E0uWLMHx48ebXnfq1ClMmzYN27dvh1rN/9vJtnr06IHx48dj1KhRiIuLQ2hoKDw8PFBQUIBdu3bhww8/xO7du0V93n//fYSGhuKFF16QdM3Jkyejf//+Fvdzd3eXdD2iKz3wwAPw9/e3qE+XLl0sev27776LZcuWidp8fHxw//33Y9asWejevTsqKyuRlpaGpUuX4pdffml6XU1NDaZOnYrDhw8jKirKoutSG+QeMiPzrF27tsVbfGVlZS2+vra2VpgxY0azPkuXLrVx5eRq3nvvPQGAoFarhdtvv13YunWrWf0++OADwc3NTfR+VavVQnp6uln9TW8Frlixoh1/CyLLmP6szcnJ6dDrFRQUCF5eXqJrdu7c+arfL2+88YagUChEfW699dYOrdMV8VagAxAEAc8++6yoLTo6GuvWrWv1NyKNRoOvv/4aAwcOFLW//PLLqK6u7rBaiZRKJf7yl7/g+PHj+OKLL5CcnGxWv/vuuw8ffPCBqE2v1+Pf//53R5RJ5NBMf5arVCqsWbMG8fHxrfZZsGABHn30UVHb6tWrcfDgwQ6r0xUxWDmANWvW4MiRI6K2t99+G76+vlftp1KpsHz5clFbQUFBszYia7r77rvx1VdfoUePHhb3vfPOO5sFsQ0bNrT6lBORKyosLGz2S8jDDz/c7Bfplrz88suipU0EQcCLL75o9RpdGYOVA/juu+9Ex127dsWUKVPM6puYmIjhw4eL2lauXGm12ohMqVSqdvWfO3eu6LiyshJHjx5t1zmJnMmaNWtEv2woFIpmI1Gt8fb2xl133SVq27BhA6qqqqxaoytjsLJz9fX1+Pnnn0Vtlq5LNW/ePNFxampqm2sKEcllwIABzdry8/NlqITIPq1Zs0Z0PHz4cHTv3t3s/qafCXV1ddi0aZM1SiMwWNm91NRUlJeXi9pGjRpl0TlMX280GrFx48Z2VkbUMTw9PZu1abVaGSohsj86nQ6//fabqM3Sz4TY2NhmTyD+9NNP7S2NLmOwsnOmt0BUKhWGDBli0TliY2MRFhYmaktLS2t3bUQd4dy5c83aQkNDZaiEyP6cPHkSdXV1orZhw4ZZfB7TPvxMsB4uaGTnMjMzRcfR0dGStiKIi4tDYWFhq+clshctbUsTGxtr8XkEQUB6ejr279+PwsJCaLVaBAQEICgoCHFxcbj22mvbPR+MqDV1dXXYuXMn0tPTUVRUBIPBgKCgIAQHB2PgwIHo2bOnpPO29LM7Li7O4vOY9jlx4oSkeqg5Bis7d+Uin0BDsJLCdAE4BiuyRwaDAf/73/9EbXFxcYiJibH4XPfdd99VV6P29fXFhAkT8I9//MPiUWCitvTr1++q77+IiAjMmDEDCxYssOjnuulngkqlQufOnS2uz/QzobKyEhcuXJB0LhLjrUA7l5eXJzqWukKu6TfuhQsXJNdE1FE++ugj5ObmitpmzZol6VxtbfFRWVmJ77//Htdffz1uvvlmFBcXS7oOUUvaev/l5+fjnXfeQY8ePfDUU0+ZvSWN6WdCRESEpJHXlsIcPxesg8HKzpk+Auvn5yfpPKb9ampqJO/lRtQRzp8/jyeffFLUFhAQgEceeaTDr71+/XokJiY2Wy+OqKPV19dj0aJFGDNmTLMHlVrSUZ8JLZ2bpGGwsnOmb/SWnpgyR0v9+E1E9kKv1+Ovf/1rsw+WV155BYGBgRadq2fPnpg/fz6+//57ZGZmorS0FPX19bh06RLS0tLw/vvvY/To0c36nT9/HjfeeGOzEQEicymVSlx//fV48cUXsWHDBpw5cwaVlZXQ6XQoLCxEamoqXnvttRbnV+3YsQMzZsxoc+SKnwkOQOYtdagNpnunPfPMM5LOs3z58mZ7WV24cMHK1RJJ87e//a3Z+3PChAmC0Wg0+xyLFy8Wdu7cafbrt2zZInTu3LnZdUeMGCHlr0Au7oknnhBOnz5t1msNBoPw7rvvChqNptn779lnn71q3/Hjx4teP2zYMEn1ZmVlNbv2l19+KelcJMYRKyKS1dKlS/Huu++K2rp06YIvvvjCooVw58+fj6FDh5r9+lGjRmHfvn3N5prs2LGDa/qQxV5//XV069bNrNcqlUo89NBD2LBhA9zc3ER/tmTJEi7g7OAYrOyc6dIKtbW1ks5TU1PTrM3Hx0fSuYis5ZtvvsHf//53UZu/vz9++uknhISEdPj1IyIi8P333zcLcIsXL+7waxONHj0ar7zyiqhNq9Xi/fffb7UPPxPsH4OVnTN9o7f0zWAOfhORvdm4cSPmzJkjeojC09MT69atQ0JCgs3qGDx4MKZOnSpq27lzJ1d7J5t45JFHRJsiA7jqzhj8TLB/DFZ2zvSNXlFRIek8pv08PT2hVPL/fpLHH3/8gVtvvRX19fVNbW5ubli5ciVGjBhh83qmT58uOtbpdNi1a5fN6yDXo9FocNNNN4na9u7d22pg6qjPhJbOTdLwk9XOmf4mY7rGj7lM+5mel8hWDhw4gBtvvBHV1dVNbUqlEp9//jluvPFGWWpKSkpq1sY1fchWTN9/BoNBtFPGlUx/dufn58NgMFh8zZY+S/i5YB0MVnauT58+ouOW9lEzh2k/KVsgELXXsWPHMGHChGa/Lb///vu47bbbZKqq5b0Ii4qKZKiEXJEl7z/TzwSDwSDplwDTzwRfX99mGzOTNAxWds40AOXm5kqa+2G6hY3pNydRRzt16hTGjx+PS5cuidoXL16Me++9V6aqGgiC0KzNkicSidrDkvdfS78Um25zYw7Tz4TevXtbfA5qGYOVnTOdxKvX67F3716LzpGdnY2CggJRW//+/dtdG5G5cnNzMW7cOOTn54va//Wvf2H+/PkyVfWnlm672OKpRCLAsvdfr169oNFoRG1S5gPu3LlTdMzPBOthsLJzSUlJ8Pf3F7Vt3brVonNs27ZNdKxUKjFx4sT2lkZklsLCQowdOxZnz54VtS9YsADPP/+8TFWJpaamNmuLiIiQoRJyRabvP5VK1eLtQQBwd3fH2LFjRW2WfiacPn262RwrueY3OiMGKzvn5uaGyZMni9q++OKLFoeOW/PZZ5+JjpOSklr9piWyppKSEowfPx5ZWVmi9vvvvx9vvPGGTFU1991334mO3dzcWpzQTmRtNTU1WL9+vaht8ODBV92qZsqUKaLjHTt24PTp02Zf0/QzQaPRYMKECWb3p6tjsHIAM2fOFB3n5ORg3bp1ZvU9cuQItm/fLmqbMWOG1Wojak1VVRUmTZqEtLQ0Ufvtt9+O9957T6aqmktNTcXatWtFbcOGDYOvr69MFZEreeutt5pN1WjrjsKUKVPg7u7edCwIAv773/+adb3q6mp8/PHHorZJkyZxqQVrkndHHTKH0WgUEhISRHs6xcTECJWVlVftp9frhUGDBon6hYeHC1qt1kaVk6uqqakRRo0a1WwvsmnTpgl6vd6q16qoqBDKy8sl9T137pwQGRnZrM61a9datUZyXvn5+ZLf05s2bRLUarXovefl5SXk5+e32ffhhx8W9VOr1cLBgwfb7Pf444+L+ikUCuHAgQOS6qeWMVg5iLVr1zb74T969OhWP1Dq6uqEWbNmNeuzdOlSG1dOrqa+vl646aabmr33Jk2aJNTV1Vn9eocOHRICAgKEF154waKNxTdu3CiEhYU1q3P48OFWr5Gc15tvvin06tVL+OSTT9r8ZbdRfX29sGTJEsHNza3Z++///u//zDpHfn6+4OXlJeobFRUlHDt2rNU+S5YsERQKhajPrbfeatb1yHwKQbBgsg7Javr06Vi1apWoLSYmBgsWLMDYsWMRGRmJwsJC7Ny5E4sXL0ZGRobotUlJSdi+fTvUarUtyyYX88wzzzTb/0ytVuOhhx666ryRqxk4cGCrt7APHz6MxMREAA0PZgwbNgxjx45FYmIievXqhU6dOsHHxweVlZXIy8vDzp078fXXX2P37t3NztWlSxfs3r0bnTt3llQnuZ633noLjz/+OICGHS0mTpyIYcOGITExETExMfD394eHhwdKS0tx+vRpbN26FStWrGj2MAcA3HDDDVi/fn2zjZlb89///hePPPKIqM3X1xcPPPAAZs2ahW7duqGqqgpHjx7F0qVLsXnzZtFrAwMDcejQoWYbkVM7yZ3syHwVFRXCdddd1+w3HHO+YmNjhby8PLn/CuQC5s6dK+k9erWvuXPntnq9Q4cOWeUaXbt2FdLT0233D0VO4c0337TK+2/MmDFCRUWFxdd/8MEHJV3P09NT2Lp1awf8ixAnrzsQX19f/Prrr802jG3LiBEjsHPnTj4+TtQChUKBOXPm4MiRI4iPj5e7HHIxXl5eWLRoEX755RdJD0y8++67eOGFF6BSqczuExUVhS1btiA5Odni61HbGKwcjK+vL1avXo01a9Zg6NChV31tQkICPv30U2zbtg1hYWE2qpDItnr37o2vvvoK9957L+Lj483+gImJicFjjz2GkydP4rPPPoOfn18HV0rOaNasWVi2bBlmzJiBmJgYs/oolUoMGDAAr7/+Os6fP49//vOfUCqlfRwrFAo899xzOHjwIGbMmCF6WtBUREQE/vWvfyEjIwNDhgyRdD1qG+dYObi8vDzs3r0bZ86cgVarhaenJ6KiojB48GB0795d7vKIbK62thYnTpzA2bNnkZeXh8rKStTW1sLb2xsBAQEICQnBwIEDOYJLHaKsrAyZmZnIzc1FQUEBtFot9Ho9/Pz8EBAQgC5dumDQoEEdtpxHZWUldu3ahaysLJSXl0OlUiEsLAwDBgxAYmIit2qyAQYrIiIiIivhrUAiIiIiK2GwIiIiIrISBisiIiIiK2GwIiIiIrISBisiIiIiK2GwIiIiIrISBisiIiIiK2GwIiIiIrISBisiIiIiK2GwIiIiIrISBisiIiIiK2GwIiIiIrISBisiIiIiK2GwIiIiIrISBisiIiIiK2GwIiIiIrISBisiIiIiK2GwIiIiIrKS/weVsrCNqV6pnAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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qq6sxbtw4rF271qnzMjMzMWrUKJSWlra3BJeaN28eysu9b1sNoo7GuhzAqN5R7brOo+P7QqNSIPPgKbcuHCoIAp77pnE+2KRLuqJHVIjb7tVe1vWsuOQCkfdpd7C68847sW3bNlFbYmIiXn/9dezduxfl5eXIzc3FihUrkJycLHrd4cOHMWXKFJhMnn+cuiXffPMNVq9eLXcZRD7B+tRav6bJ1lL1iArBjOE9AAAvfrsfZot7phF8v68U2wrOQqtW4qFrLm77BBmxx4rIe7UrWGVkZGDNmjWitjFjxmDPnj148MEHMWDAAERERCA5ORmzZ8/Grl27MG3aNNHrs7OzsWzZsvaU4RLV1dW47777bMf9+/dHfHy8jBURdVwVtQ0ormx8kq9vnK7d15s3tjfCAtXIK9VjbdNeg65UbzTjhW/3AwDuubwXunrh3KrzWZ8MzCupgslskbkaIjqf5GAlCAKefPJJUVtiYiIyMjIQHt7yT6harRaffvopUlNTRe0vvPACamtrpZbiEo8//jgKCwsBNE6aXbZsGTQajaw1EXVU1iGqxE7BCAts/9dRRHAA5o29CADw6g8HUGNwbS/3yqyjKDxbh5gwLeZ68dwqq+6dghGqVcNgsiD/dI3c5RDReSQHq3Xr1mH37t2itjfeeAM63YV/OlWpVFixYoWorbS0tFmbJ23duhVLly61Hd91110YNWqUbPUQdXTWYcBkF/RWWc1I647ETsGNi4b+mu+y657U1+PtjY1PAj46vi+CA+TfE7AtSqXC9m/LFdiJvIvkYGU/F6lHjx6YNGmSQ+cOHjy4WXCxH1L0FKPRiDlz5sBiaexO79KlCxYuXChLLUS+wtpj1S+uffOrzidaNDQz32WLhi76/iCqDSYMSojAXy5pfbsdb8MV2Im8k6RgZTQa8e2334raZsyY0eZWFeezXxMqOzsbJ0+elFJOu7z88svIycmxHS9atAidOnXyeB1EvmR/iR7AuafXXOXagbG4NDECdUYzFv1wsN3XyymqxOqdjVMAnrq+H5RKx7+HyY0rsBN5J0nBKjs7G5WV4u7n9PR0p65h/3qLxYINGzZIKUeyAwcO4IUXXrAdX3HFFZgxY4ZHayDyNQ0mCw6fdE+wUigUeOK6fgCA1TsLsTX/jORrCYKAZ7/JhSAAEwfFI7V7pKvK9Ih+5z0Z6M0LLhP5G0nBas+ePaJjlUqFYcOGOXWNpKQkxMTEiNr27t0rpRxJBEHA3XffDYPBAKBxYr03PJ1I1NEdOqmH0SwgLFCN+PBAl18/tXskbkztBkEA7v/kD5RWShsS3JBTiu0FZxGoUeKxCX1dXKX79YnRQaNSoLLOiKKKOrnLIaImkoJVXp54BeTExESEhDi/mJ79ulb213Wnd999F5s3b7YdP/bYY+jTp4/H7k/kq86tXxXm1PQAZzw3aQD6xupwuroBf/vkDzSYnFtyoN5oxovfNS6vcPflSV65dU1bAtRK9I62TmDncCCRt5AUrPbv3y86TkxMlHTzhIQE0bGnglVJSQkeffRR23GfPn3w+OOPe+TeRL7ONr/KhRPX7QUFqLDstlToAtXYeawcL367v+2TzvNeVgEKz9YhNiwQc8d430bLjrIuFMoJ7ETeQ1KwKi4uFh3bByRH2QeyoqIiSddx1v333y+aI7Z06VJotVqP3JvI1+WWNH5tuXKphZb0iArBa9MvAQCs2nIUX+1ybOHQk/p6vN200fKjEy7uEMsrtIYrsBN5H0nBqrq6WnQcFiZtgqr9eXV1dbZlD9zl66+/xpdffmk7vu222zB27Fi33pPIXwiCIBoKdLdx/WLwwJW9AQCPf7kX+9vYO++U3oBHv9iDmgYzBiVEYNKgjrO8QkusTwbmci0rIq/hkmAVFCRtfkJL59lf25Wqqqrwt7/9zXYcGRmJRYsWue1+AGAwGFBVVSX6IPJVRRV1qKo3QaNS4KJo9/ZYWf19XB9c3qcL6o0WzP1oJyrrjM1eU9dgxlu/HEL6/23ExgOnoFIqOtzyCi2x9goWV9ajvKZB5mqICJAYrGpqxFsoBAZKe/LH08Hq0UcfFQ1jLly4ENHR0W67HwC89NJLCA8Pt31IHTYl6gis86t6R+sQoG73Hu8OUSkVeOOmS9A1IgjHztRiweo/YWnaqNliEfDlHydw5aJNePWHg409Vd3C8fndwzvc8got0QVq0L1zMAAOBxJ5C8985/MCv/32G5YvX247HjlyJGbPnu32+z7++OOorKy0fVj3IyTyRe7YysYRkSEBWHZbKgLUSvy0/yTe3ngYW/PPYNLbWZi/ejdKKuvRNSIIb9x8Cb66bySG9PCdRYBtE9hLOBxI5A0kzdoMCQlBRUWF7bi+Xto6MnV1zddeCQ0NlXStCzEYDJgzZ45tET2NRoPly5e77VHw82m1Wk6MJ79hfXO3brfiSQO7heP5SQPwyNo9WPTjuVXZQ7Vq3HdFEu4c2ROBGpXH63K3/vHh+HZvKXusiLyEpGAVGhoqClYtBSRHeCpYvfjii6KlHBYsWID+/fu7/D5E/s5dW9k4avrQBOwqrMCn249DpVTgr5cl4sFxFyEq1Hd/uLGG2Lymf3sikpfkYHU+qROy7c8LCgqCUuna0cnc3Fy8/PLLtuOePXviqaeecuk9iAioqjfi+NlaAPL0WFk9O6k/hvfqhP7x4egd7fof1LxNYtMcq2Kuvk7kFSSlmPj4eNGx1HlD9ufZX9cVZs+ejYaGc0/LvP3225KfYiSi1ll7TOLDAxERHCBbHRqVEpMu6eoXoQoA4sMbv5/pDSZU1Td/IpKIPEtSj1Xfvn3xyy+/2I6PHz8u6eb259lvceMK2dnZtt9HRUUhMzMTmZmZDp1bXl4uOt65cycee+wxUdu0adOQmpra/kKJOjjrWkpyDQP6q6AAFSKDNSivNaK4og5hsRq5SyLya5KClX0AKiwsRE1NjdP7BdpvYdO3r3s3Qj19+jQWLlwo+fycnBzk5OSI2vr27ctgRYTzt7JhsPK0+IggW7DqG8t/fyI5SRoKTElJER2bTCZs377dqWscOXIEpaWloraBAwdKKYeIvEBuiXWpBb6xe5p1E+niCmlPaBOR60gKVmlpaQgPF2+wumnTJqeuYT8cp1QqMX78eCnlEJHMTGYLDpTJ+0SgP+tqC1acwE4kN0nBSqPR4NprrxW1ffjhh7Z1ohzx/vvvi47T0tLcsgq6IAiSP7p37y661syZM5u9ZtasWS6vmaijyT9dgwaTBaFaNRIig+Uux+/EhTfufsFgRSQ/yWsbTJ8+XXRcUFCAjIwMh87dvXs3Nm/eLGqbNm2a1FKISGbnr7je0fff64g4FEjkPSQHq0mTJjWbazVv3rw29/ozm83NtpKJjY3FnDlzHLqvQqEQfaSnpztVNxG5HudXycsarIrYY0UkO8nBSqFQ4Pnnnxe1HTt2DBMnTmx1wdCGhgbceuut2LFjh6j9iSeeQHAwhw+IOiprjxWfCJSHdY5VWVU9zBbHp2QQkeu1a5nzG264AVOnThW1bdy4ESkpKViyZAlyc3NRUVGBAwcO4L333sPgwYPx+eefi16flpaGuXPntqcMIpKRIAjY39RjxYnr8uii00KtVMBkEXBKb5C7HCK/Jmkdq/OtXLkShYWFouUWjh07hnnz5rV5blJSEtauXQu1ut1lEJFMTuoNOFPTAKUC6BOjk7scv6RSKhATFoiiijoUVdQhtmkyOxF5Xrs35tPpdPjpp58wefJkp84bPXo0srKyEBcX194SiEhG1mHApC6hCNSoZK7Gf3HJBSLv4JIdj3U6Hb788kusW7cOI0aMuOBrU1JSsGrVKmRmZiImJsYVtyciGeVyGNArxEdwyQUib+DSMbiJEydi4sSJKC4uxtatW3H06FHU1NQgKCgICQkJGDp0KHr16tWuezizVlZ7HT161GP3IuqobMGKE9dlFc8eKyKv4JbJTfHx8ZgyZYo7Lk1EXmZ/MZda8Aa2YFXJtayI5OSSoUAi8k+1DSYUnKkBwGAlNw4FEnkHBisikiyvVA9BAKJ1WnTRaeUux69xKJDIOzBYEZFktoVBOXFddtZgVV5rRG2DSeZqiPwXgxURScatbLxHWKAGOm3jtFnuGUgkHwYrIpKMW9l4F2uvVUklhwOJ5MJgRUSSCIKAg2V6AEByHFdc9wacwE4kPwYrIpKkqs6E2gYzAKBbJDdR9wZxTT1WRRwKJJINgxURSVJS1dgrEhms4VY2XoLb2hDJj8GKiCQpbVqIMiaMG/56Cw4FEsmPwYqIJLEGq7hwBitvER/OHisiuTFYEZEkpVWNwSq26c2c5Hf+tjae3FeViM5hsCIiSaw9VrEcCvQaseGBUCiABpMFZ2oa5C6HyC8xWBGRJCUcCvQ6GpUS0U1bC3E4kEgeDFZEJElZ01BgDIOVV+GegUTyYrAiIknYY+Wd4rmWFZGsGKyIyGl1DWZU1hkBNM7rIe9hXcuqhD1WRLJgsCIip1mfCAwOUNk2/iXvYO1BLOZ+gUSyYLAiIqdZN/ltfApNIXM1dD4OBRLJi8GKiJxmnbjOpRa8D7e1IZIXgxUROc06cZ3zq7yPtcfqlN4Ag8ksczVE/ofBioicVsYnAr1W46bYjd/arYu4EpHnMFgRkdNKuOq611IoFOetZcVgReRpDFZE5LQy7hPo1bgZM5F8GKyIyGnssfJu8RFNSy4wWBF5HIMVETnFaLbgVLUBACeveyvbUCDXsiLyOAYrInLKKb0BggBoVAp0DgmQuxxqAdeyIpIPgxUROcU6DBitC4RSycVBvRG3tSGSD4MVETnFOnGdSy14L9u2NhV1EARB5mqI/AuDFRE5xdpjFcNg5bWsQ4E1DWZU1ZlkrobIvzBYEZFTSpsmRMfxiUCvFahR2ea/FXE4kMijGKyIyCmlVXwisCOI556BRLJgsCIip1h7rBisvJttLSsuuUDkUQxWROSUUk5e7xDiwrmtDZEcGKyIyGEWi4CyysahwBjOsfJqXTkUSCQLBisictjZ2gY0mC1QKBrXsSLvxTlWRPJgsCIih5U2LbXQOUSLADW/fXgz7hdIJA9+ZyQih1mDFedXeT/rUGBpVT1MZovM1RD5DwYrInKYdeI6nwj0flGhWmhUClgE4KTeIHc5RH6DwYqIHGbtsYrlxHWvp1QqbAGYw4FEnsNgRUQOs25nwx6rjiG+ackFrr5O5DkMVkTkMOsGzOyx6hjOLbnAtayIPIXBiogcVmLdJ5A9Vh0Cl1wg8jwGKyJyWBn3CexQGKyIPI/Biogcoq83otpgAsBg1VHE2fYL5FAgkacwWBGRQ6xPBIYFqhEcoJa5GnIEt7Uh8jwGKyJyCNew6nisc+Eq6871NhKRezFYEZFDzi21ECRzJeQoXaAGYYGNvYsl7LUi8ggGKyJySJl1OxsutdChWCewcy0rIs9gsCIih5Q0DQXGcCiwQ7EGq1JOYCfyCAYrInJIGTdg7pBimnoYSxisiDyCwYqIHFLCfQI7JGsQZo8VkWcwWBGRQ/hUYMdk/f+y/v8RkXsxWBFRm+qNZpytaQDAHquOxvr/xR4rIs9gsCKiNp1s2spGq1YiIlgjczXkDOtQoHWfRyJyLwYrImqTdRgpLjwQCoVC5mrIGdahwKp6E2obuEgokbsxWBFRm6y9HTEcBuxwdIEahASoAHA4kMgTGKyIqE2lXGqhQ4vlk4FEHsNgRURtKuXioB1aXNM2RFzLisj9GKyIqE2l3M6mQ+OSC0Sew2BFRG06t4YVN2DuiLjkApHnMFgRUZusb8hcHLRjirUtucBgReRuDFZEdEFmi4CT+sZ1rDh5vWOy/r+VcSiQyO0YrIjogk5XG2C2CFApFYgK1cpdDknAjZiJPIfBioguyPpmHK3TQqXk4qAdkbXH6nS1AQ0mi8zVEPk2BisiuiDOr+r4OoUEIEDV+O3+pJ69VkTuxGBFRBdU2rTqOjdf7rgUCgViwhuHcflkIJF7MVgR0QWVVLHHyhfEhXGRUCJPYLAiogsqsw4FsseqQ4vlk4FEHsFgRUQXVMI5Vj6Ba1kReQaDFRFdkLWHI46rrndoXH2dyDMYrIioVYIgnOux4lBghxbH/QKJPILBiohaVVlnhKFp3aPoMC4O2pHFhLPHisgTGKyIqFXW3qpOIQEI1Khkroba4/xtbcwWQeZqiHwXgxURtaqUw4A+o0uoFkoFYLIIOFNtkLscIp/FYEVErSq1TVxnsOro1ColonWcZ0XkbgxWRNSqkorGVddjGKx8QgyXXCByOwYrImpVcdMbcNcILrXgC+K45AKR2zFYEVGrSpr2CeRQoG+I5ZILRG7HYEVErSqu4OKgviSWSy4QuR2DFRG1SBAEFDfNseJQoG+Is82xqpO5EiLfxWBFRC06W9NgWxw0JpyLg/oC67IZZVVcboHIXRisiKhF1ifHuui00Kq5OKgviD2vx0oQuEgokTswWBFRi4qahgHjOXHdZ8Q09VjVGy2orDPKXA2Rb2KwIqIWWdewiuf8Kp8RqFGhU0gAAD4ZSOQuDFZE1CLrUCCfCPQt1l4rLhJK5B4MVkTUIttQYASHAn1JHJdcIHIrBisiapG1R4NDgb6Fa1kRuReDFRG1yLqGFVdd9y2x3NaGyK0YrIioGZPZgrIq7hPoi2xLLnDyOpFbMFgRUTNlegMsAqBRKRAVysVBfcm5OVZcfZ3IHRisiKgZ61ILseGBUCoVMldDrsTJ60TuxWBFRM0U2eZXcRjQ11iXW6iqN6HGYJK5GiLfw2BFRM1Ynwjk/CrfowvUIFSrBsBFQoncgcGKiJop4ROBPs06gb2Mw4FELsdgRUTNFFU0rbrOHiufFMvV14nchsGKiJopaXpirCtXXfdJtkVCORRI5HIMVkTUTDEnr/s0PhlI5D4MVkQkUtdgRnmtEQC3s/FV3IiZyH0YrIhIpLhpGDAkQIWwQLXM1ZA72HqsqrhIKJGrMVgRkUhJxbnNlxUKLg7qi85txGyQuRIi38NgRUQi1h4rPhHou6xPBZ6uNqDBZJG5GiLfwmBFRCLWievxXMPKZ3UKCUCAqvHbfxmfDCRyKQYrIhI5fyiQfJNCoTi3SCiDFZFLMVgRkYhtKJA9Vj7NGqz4ZCCRazFYEZGIdSiQ+wT6Nus8K65lReRaDFZEZCMIAoq5nY1fiGOPFZFbMFgRkU1lnRF1RjMADgX6Os6xInIPt6z+V1JSgm3btqGgoAA1NTUICgpCYmIihgwZgp49e7rjlk4zm804fPgwcnJyUFZWhoqKCmi1WnTq1AkJCQkYNmwYdDqd3GUSeVRR0zBg55AABGpUMldD7nRuI2YuEkrkSi4NVhkZGVi4cCG2bNkCQRBafM2gQYMwf/58zJgxw+OLD+bm5uKbb77BL7/8gl9//RW1tbWtvlapVGLIkCG47777cPPNN0Or1XqwUiJ5lNiGAdlb5etiuV8gkVu4ZCiwuroaN954IyZOnIisrKxWQxUA7N69GzNnzsQVV1yBsrIyV9y+TR9//DEGDhyI/v3749FHH8X3339/wVAFABaLBdu3b8esWbPQv39/ZGdne6RWIjlZey/iufmyz7NusH1Sb4DZ0vr3bCJyTruDVXV1NcaNG4e1a9c6dV5mZiZGjRqF0tLS9pbQpoyMDOTk5Eg+/8iRIxg9ejQ+/vhjF1ZF5H2KuIaV34gKDYBSAZgsAs5Uc2sbIldpd7C68847sW3bNlFbYmIiXn/9dezduxfl5eXIzc3FihUrkJycLHrd4cOHMWXKFJhMpvaW4bRhw4bh8ccfx4YNG5CXl4ezZ8/i1KlT2LlzJ1555RUkJiaKXm82mzFr1iz88MMPHq+VyFNsPVYcCvR5apUS0To+GUjkau2aY5WRkYE1a9aI2saMGYN169YhPDzc1hYREYHk5GTMmDEDM2bMEJ2TnZ2NZcuW4f77729PKQ7p1KkT7rrrLsyePRt9+vRp8TVRUVG49NJLMW/ePMybNw/vvvuu7c9MJhPmzp2L3NxcBAbyjYd8j3UNqzgOBfqF2PBAlFbVo7SqHoPkLobIR0jusRIEAU8++aSoLTExERkZGaJQdT6tVotPP/0UqampovYXXnihzTlP7REaGoonnngC+fn5eOWVV1oNVefTarVYvnw5br/9dlF7QUEBVq5c6a5SiWRVzKFAv8JFQolcT3KwWrduHXbv3i1qe+ONN9pcokClUmHFihWittLS0mZtrrR8+XI8//zzrQa+C3n99dcREREhavv6669dUxiRFzFbBJRWWYMVe2T9Abe1IXI9ycFq9erVouMePXpg0qRJDp07ePBgjBo1StRmP6ToSiqV9PV4IiMjMXHiRFFbVlZWe0si8jqnmp4OUykVtrk35NviuEgokctJClZGoxHffvutqM3ZdalmzZolOs7OzsbJkyellON2gwaJZx/U1NRAr9fLVA2Re1g3X44NC4RK6dk15kge53qsuEgokatIClbZ2dmorKwUtaWnpzt1DfvXWywWbNiwQUo5bhcU1Hy+SU1NjQyVELnPuYnr7K3yF9aHFE6UM1gRuYqkYLVnzx7RsUqlwrBhw5y6RlJSEmJiYkRte/fulVKO2x0/flx0rFAoEBUVJVM1RO5Rwonrfuei6FAAjcFKX2+UuRoi3yApWOXl5YmOExMTERIS4vR17Ne1sr+ut8jMzBQdd+/eHWq1W7ZZJJKNdZ9AbmfjPyJDAmw9lHmlnN5A5AqSgtX+/ftFx/aLaToqISFBdOyNwergwYPNtrOZMGGCTNUQuY91nk1X9lj5leS4MADA/pIqmSsh8g2SglVxcbHo2D4gOco+kBUVFUm6jjs9/fTTzdpuuukmGSohci/rGlZcHNS/9GOwInIpScGqurpadBwWFibp5vbn1dXVwWKxSLqWO3zzzTf47LPPRG2XX345xowZI1NFRO5j7bHi5HX/Yu2xyi1msCJyBUkTheyDVUtPzTmipfOqq6slBzVXKioqwh133CFq02g0WLJkiVPXMRgMMBjObXBaVcVvXuR96o1mnK5uAMChQH+THNe4qPOBMr1tHTMikk5Sj5X9UgNS981rLVjJrb6+HpMnT8bp06dF7c888wxSUlKcutZLL72E8PBw24fUYVMid7JuaRKoUSIiWCNzNeRJ3TuHIDhAhXqjBQWnuYwMUXtJXnndV1ksFsyYMQO///67qH3ChAl47LHHnL7e448/jsrKSttHYWGhq0olchnr4qDxEUFOLfRLHZ9KqcDFsY29VrmcZ0XUbpKClf3SCvX10rZDqKtrvihdaGiopGu5ygMPPIAvvvhC1DZo0CB89tlnkt5wtFotwsLCRB9E3sa2+TInrvslPhlI5DqSgpV9+GkpIDnC24LVU089haVLl4rakpKSsGHDBgYi8mklFdYeK05c90d8MpDIdVwSrKROyLY/LygoCEqlPKOTixcvxnPPPSdqi4+Px48//ojY2FhZaiLylGLbE4HssfJHfDKQyHUkpZj4+HjRsdR5Q/bn2V/XU1asWIEFCxaI2qKiovDjjz+iZ8+estRE5EnWoUA+Eeif+sbqoFAAJ/UGnKk2tH0CEbVKUrDq27ev6Nh+Lz1H2Z9nv8WNJ3z66aeYO3euqC0sLAwbNmxAv379PF4PkRyKuZ2NXwvRqtGjc+Pc2f0l3NqGqD0kBSv7AFRYWNhsCQZH2G9hYx/Y3G3dunW4/fbbRYuSBgcH45tvvkFqaqpHayGSU0klV133d9b1rHJLKmWuhKhjkxSs7NdyMplM2L59u1PXOHLkCEpLS0VtAwcOlFKOJD/88ANuuukmmEwmW1tAQAC++uorjB492mN1EMmtqt6IakPj1wEnr/uv5FjrBHb2WBG1h6RglZaWhvDwcFHbpk2bnLpGZmamuBClEuPHj5dSjtN+++03TJ48WbQiulqtxmeffYarr77aIzUQeQvrMGBEsAbBAZI2YyAf0C+eTwYSuYKkYKXRaHDttdeK2j788EMIguDwNd5//33RcVpaGqKjo6WU45SdO3fiuuuuQ21tra1NqVRi1apVmDx5stvvT+RtSriGFeHck4GHT1bDYDLLXA1RxyV5bYPp06eLjgsKCpCRkeHQubt378bmzZtFbdOmTZNaisNyc3Mxfvz4Zss8vPPOO7j11lvdfn8ib1TENawIjZtvhwdpYLIIOFQm/9ZiRB2V5GA1adKkZnOt5s2b1+Zef2azGbNnzxa1xcbGYs6cOQ7dV6FQiD7S09MdOi8/Px9XXXVVs/3/Fi9ejLvvvtuhaxD5opLztrMh/6VQKLhQKJELSA5WCoUCzz//vKjt2LFjmDhxYqsLhjY0NODWW2/Fjh07RO1PPPEEgoODpZbSpuLiYowbNw7FxcWi9ueeew7/+Mc/3HZfoo7AuoYVnwgk20KhDFZEkrVrpuoNN9yAqVOnYu3atba2jRs3IiUlBQsWLMDYsWMRHx+PsrIyZGVlYdGiRcjNzRVdIy0trdk6Uq42bdo0FBQUiNri4uJQXV0taWNl6zW5JAP5gmIOBVIT65IL7LEikq7djwCtXLkShYWFouUWjh07hnnz5rV5blJSEtauXQu12r1PIhUVFTVrKykpwcKFCyVfs2/fvgxW5BOsa1hxKJDOPRmohyAIkjaeJ/J37d6YT6fT4aeffnL6ibrRo0cjKysLcXFx7S2BiCQymS22OVZx4eyx8ne9o0OhVipQWWdEcVPgJiLnuGTHY51Ohy+//BLr1q3DiBEjLvjalJQUrFq1CpmZmYiJiXHF7YlIoqNnamA0CwgOUHG5BYJWrULv6FAAwH5uyEwkiUvH4CZOnIiJEyeiuLgYW7duxdGjR1FTU4OgoCAkJCRg6NCh6NWrV7vu4cxaWVZHjx5t1z2JfFVu0yrbF8fqoFRy2IeAfnFhyCvVY39JFcb14w+/RM5yy+Sm+Ph4TJkyxR2XJiIXsk5Stj4NRpQcFwbsKuKTgUQSuWQokIg6pjxrsIrVyVwJeYtkrmVF1C4MVkR+zLrhLnusyMq65MKxs7W2zbmJyHEMVkR+qrymAaVVjU9+XcweK2rSOVSLmDAtBAE4UMpeKyJnMVgR+an9TW+aCZ2CoAvUyFwNeZNzK7DrZa6EqONhsCLyU3lNb5p9YzkMSGLcM5BIOgYrIj/FJwKpNbYeK65lReQ0BisiP5VX2thj1S+O86tIzBqsDpTqYbY4v3YgkT9jsCLyQyazBQfKOBRILesZFYJAjRJ1RjOOnamRuxyiDoXBisgPHT1TgwaTBcEBKiR2Cpa7HPIyKqUCF8daJ7BzOJDIGQxWRH6IW9lQW6xDxJzATuQcBisiP8SJ69SWc08GcskFImcwWBH5IW5lQ23hk4FE0jBYEfkhbmVDbenb9LlRWlWP8poGmash6jgYrIj8DLeyIUeEatXo3rnxwQZOYCdyHIMVkZ/hVjbkqEsTIwEAn/9eKHMlRB0HgxWRn7FuZZPM9auoDbNH9wQAZOwpxuGTnMRO5AgGKyI/Y30isC/nV1Eb+seH45r+MRAE4I2fD8tdDlGHwGBF5Ge4lQ0548GxfQAA3+wpxqEy9loRtYXBisiPcCsbcla/+DCM7x8LQQDe/IW9VkRtYbAi8iPcyoakeHDcRQAae60OsteK6IIYrIj8CLeyISmS48IwYUBTr9XPh+Quh8irMVgR+RFuZUNSzRvb2Gv1v70l7LUiugAGKyI/wq1sSKrkuDBcOzC26QlB9loRtYbBisiPcCsbag9rr9W3e0twoJS9VkQtYbAi8hPcyobaq2/suV4rzrUiahmDFZGf4FY25ArWda3+x14rohYxWBH5CW5lQ65wcawO1w2MAwC88fNBmash8j4MVkR+glvZkKvMG3sRFArg272lyGvqCSWiRmq5CyAiz+BWNuQqF8fqcO3AOPxvTwnuWrUDKd3C0S0yCN0ig22/do0MQqiWbzHkf/hZT+QHuJUNudrfx16EH/eVoaiiDkUVdS2+Jio0AIMTIzGsZycM79UZyXFhUHFhWvJxDFZEfoBb2ZCrXRSjwy8PjcH+Ej1OlNeiqLwOJ8rrcKKiFifK61BRa8Tp6gb8mFuGH3PLAAC6QDUu69EJw3p1wrCenTGgaziDFvkcBisiP8CtbMgdGof+Wg7q+nojDpZVY3vBWWwrOIMdR8uhrzfh57yT+DnvJAAgtXskPrprGIICVJ4sm8itGKyI/AC3siFP0wVqkNo9EqndI3FvehJMZgtyS6qwLb8xaG05cgY7j5XjkbV78ObNl0ChYOAn38CnAon8ALeyIbmpVUqkdIvAnMt74T8zh2LlrKFQKxXI2F2M5Zvz5S6PyGUYrIj8ALeyIW8zrFdnPD2xPwBg4YY8bDxwUuaKiFyDwYrIx3ErG/JWtw1LxC2XJUAQgHmf7kL+qWq5SyJqNwYrIh/HrWzIWykUCjwzcQCGdI+Evt6EOR/sgL7eKHdZRO3CYEXk4/YVWedXcRiQvE+AWomlt12K2LBAHDlVg79/9icsFkHusogkY7Ai8mGCIODzHYUAgOG9OstcDVHLonWBePf2VGjVSvycdxKLf+QehNRxMVgR+bDMg6dw+GQ1QrVqTBvSTe5yiFqV0i0CL08dCAB4a+Nh/G9PicwVEUnDYEXkw/77WwEA4KahCZxfRV5v8uBumDO6JwDgoTW7cahpGyaijoTBishHHSjV49dDp6FUALNG9JC7HCKHPDq+L0b27ow6oxlv/HxI7nKInMZgReSj/vtb46KLEwbEIYH7A1IHoVYp8cS1/QAA3+4twfEztTJXROQcBisiH3RKb8DXu4oBAHeO6ilzNUTO6Rcfhsv7dIFFAP7zG1dlp46FwYrIB3249RgazBYMToxAavdIucshcto9l/cCAKzeUYizNQ0yV0PkOAYrIh9TbzTj463HAACzR/WSuRoiaUYkdcaArmGoN1rwQfZRucshchiDFZGP+XpXEc7UNKBrRBCu6R8jdzlEkigUCtx9eRIA4IPsY6hrMMtcEZFjGKyIfIggCPhP0xILd4zsAbWKX+LUcV07IBbdIoNwtqYBX+wslLscIofwuy6RDzl/QdDpQxPkLoeoXdQqJWY3PXyx4tcCmLnVDXUADFZEPsS6IOj0IQkI44Kg5AOmD01ARLAGx8/WYkNOqdzlELWJwYrIR5y/IOgdI3vIXQ6RSwQHqHF7Wg8AwLubj0AQ2GtF3o3BishHWBcEHT8glguCkk+ZmdYdWrUSu09UYmv+WbnLIbogBisiH3BKb8DXfzYuCHoXl1ggH9M5VGvbRHz55iMyV0N0YQxWRD7go63H0GCy4JIELghKvmn2qF5QKIBNB04hr7RK7nKIWsVgRdSBHT9Ti3+v34dlmY0/xc8eze1ryDf1iArBhAGxAIB3N3ObG/JeDFZEHdDOY+W496OdSH91I1ZtOQqDyYKRvTtjfP9YuUsjchvrgqHr/yxGSWWdzNUQtUwtdwFE5BizRcCPuaVY8WsBdh4rt7Vf3qcL7h7dCyN7d4ZCoZCxQiL3uiQhAsN6dsK2grN477cCPHFdP7lLImqGwYrIhQ6W6ZF/qhpmC2AWBAiCALOl8UMQAAECtGoVAjVKBGpUCNSoEHTerwaTGaerG3CmxoDTegPO1DTgdLUBp6sbsL+kCifKG39KD1ApMemSeMwe3QsXx+pk/lsTec49Y3phW8FZfLq9EA+O64NQLd/GyLvwM5LIBYoq6vDKhjysa3oyz13CgzS4bXgiZqb1QHRYoFvvReSN0vtEo1dUCPJP1+CrP05gRtMaV0TegsGKqB2qDSa8s+kw/vNrAQwmCxQKYFC3CASolVApFFApFVAoAJVSAVXTMJ3BZEG90Yw6oxn1RjPqjRbUGc2oazBDo1IgSqdFVKgWUaEBiArVonOIFp1DAxATFoiRvTsjOIBftuS/lEoFZqR1xzMZufgg+xhuG96dQ+DkVfgdmkgCs0XAmh2FePWHgzhdbQAADO/VCf+6rh8GdA2XuToi3zY1tRv+7/sDOHSyGtn5ZzAiKUrukohsGKyInPTbodN4/n+5yCvVAwB6RoXg8Ql9cVW/GP7kTOQBYYEaTB7cFR9vO44Ps48xWJFXYbAicsKyzCN4+bs8AI3znR4cexFuG94dAWquXELkSben9cDH247jh9wylFTWIS48SO6SiABwHSsihx0o1WPRDwcAADOGd0fmw+m4c1RPhioiGVwcq8Ownp1gtgj4ZNtxucshsuE7ApEDzBYBj6zdA6NZwFX9YvDspP6ICA6QuywivzZzRA8AwKfbj8NgMstbDFETBisiB7z3WwF2F1ZAF6jG838ZwLlURF7gqn4xiAnT4nR1AzbklMpdDhEABiuiNh09XYNXm4YA/3VdMmK4fhSRV9ColLh1WHcAwPtbjspbDFETBiuiC7BYBDy6dg8MJgtG9Y7C9CEJcpdEROe5+bIEaFQK/HG8AjlFlXKXQ8RgRXQhn2w/jm0FZxGkUeGlKQM5BEjkZaJ1gZgwIA4A8EH2UXmLIQKDFVGriivqbEsrPDL+YiR0Cpa5IiJqycwRjcOB6/4sRnlNg8zVkL9jsCJqgSAIeOKrvag2mJDaPRK3cz8yIq91aWIk+sWFwWCyYM3OQrnLIT/HYEXUgq//LMLGA6cQoFJi4dSBUCk5BEjkrRQKha3X6sOtx2C2CDJXRP6MwYrIzim9Ac9k5AIAHhx3EXpH62SuiIjaMnFQV4QHaVB4tg6ZB0/KXQ75MQYrIjv/Xr8PFbVG9IsLw92X95K7HCJyQFCACtOHdAMAvL/lmMzVkD9jsCI6z94Tlfjf3hKolAq8cmMKNCp+iRB1FLcN7w6FAsg8eApHT9fIXQ75Kb5rEJ3no62NP+lenxKHAV3DZa6GiJzRvXMI0vt0AQC8l1UgczXkrxisiJpU1hmxfncxANhWcyaijmXO6Mbh+89/L8TpaoPM1ZA/YrAiavLVHydQZzSjT0wohvaIlLscIpIgLakzBnULh8Fkwaqso3KXQ36IwYoIjetWfbTtOADrPA0ur0DUESkUCtyb3htA40rs+nqjzBWRv2GwIgKwreAsDp+sRnCACpMHd5W7HCJqh6v7xaBXlxBU1Zvw6fbjcpdDfobBigjnJq1PuqQrdIEamashovZQKhWYOyYJAPCfXwtgMJllroj8CYMV+b1TegO+31cKALh1WKLM1RCRK/zlkq6IDQvESb0BX/1RJHc55EcYrMjvrd5RCKNZwCUJEVxigchHBKiVmD26JwBg+eZ8bnNDHsNgRX7NbBHwyXmT1onId9xyWSIigjUoOF2DDTmlcpdDfoLBivxa5sGTKKqoQ3iQBtenxMldDhG5UIhWjZlpPQAA72QehiCw14rcj8GK/NpHWxt7q6aldkOgRiVzNUTkajNH9ECQRoWcoir8dvi03OWQH2CwIr9VeLYWGw+cBAD8lZPWiXxSp5AA3HxZAgBg6cYjMldD/oDBivzWZ78fhyAAI3t3Rq8uoXKXQ0RuMnt0L6iVCmTnn8Gu4+Vyl0M+jsGK/FKDyYLPfy8EANzGfQGJfFrXiCBMuqRx4d9lmey1IvdisCK/9P2+UpyubkC0Totx/WLkLoeI3Oze9MbNmb/fV4bDJ/UyV0O+jMGK/JJ1pfWbL0uERsUvAyJf1ztah6ubfohalpkvczXky/iOQn7nUJke2wrOQqkAbh6aIHc5ROQh96Y3bnPz1a4i7DlRIW8x5LMYrMjvfJDd2Fs1NjkG8RFBMldDRJ4yODES16fEwWwR8MgXe9BgsshdEvkgBivyK6WV9bZJ63eM7CFvMUTkcc9M7I9OIQHIK9Xj7Y2H5S6HfBCDFfmVpZsOo8FswWU9OyGtV2e5yyEiD+scqsUzE/sDAN7eeBj7S6pkroh8DYMV+Y2Syjp8tr2xt+rv4y6CQqGQuSIiksP1KXG4ul8MTBYBD3+xGyYzhwTJdRisyG+8s+kIe6uICAqFAs//ZQDCAtXIKarCu7/yKUFyHQYr8gvFFeytIqJzosMC8dQNjUOCr/90iGtbkcswWJFfsPZWDevZCSOSouQuh4i8wNRLu2JMny5oMFnwyBd7YLYIcpdEPoDBinxecUWd7UnAv4/rI3M1ROQtFAoFXpoyEKFaNf44XoFVW47KXRL5AAYr8nnn91alJXFuFRGdEx8RhH9emwwA+L/v83DsTI3MFVFHx2BFPo29VUTUllsuS8CIpM6oNzYOCVo4JEjtwGBFPs26btXwXuytIqKWKRQKvDwlBUEaFbYVnMXrPx2UuyTqwBisyGed31v14Fj2VhFR6xI7B+Nf1zcOCb75y2Es/vEgBIE9V+Q8BivyWUs3HYbRLLC3iogccuuw7vjntX0BAG/+fAiLfmC4IucxWJFPKmJvFRFJcPflSfjXdY09V29tPIxXvj/AcEVOUbvjoiUlJdi2bRsKCgpQU1ODoKAgJCYmYsiQIejZs6c7btku+/fvxx9//IGSkhIYDAbodDokJSUhLS0NnTp1krs8kmDpRvZWEZE0s0f3glKhwLPf5OKdTUdgsQh4bEJfLixMDnFpsMrIyMDChQuxZcuWVhP+oEGDMH/+fMyYMUPWT1Kj0Yh33nkHb775Jo4cOdLia1QqFa6++mr885//xKhRozxcIUm1Lf8MVu/gk4BEJN2do3pCpVTg6fX7sHxzPswWAU9cl8xwRW1SCC7o46yursasWbOwdu1ah88ZM2YMPv/8c8TExLT39k47dOgQpkyZgpycHIfPuf/++7F48WJoNJp23buqqgrh4eGorKxEWFhYu65Fze09UYlbVmxFtcGE8f1jsWxGqtwlEVEH9uHWY3jy68b3ijtG9sBT1/djuPJTjr5/t3uOVXV1NcaNG+dUqAKAzMxMjBo1CqWlpe0twSn79+/HiBEjnApVAPDWW29h2rRpMJlMbqqM2utQmR63v7cN1QYThvXshNdvvkTukoiog5sxvDtenDwQALAy6yj++dVe1BvNMldF3qzdwerOO+/Etm3bRG2JiYl4/fXXsXfvXpSXlyM3NxcrVqxAcnKy6HWHDx/GlClTPBZWqqqqcMMNN+D06dOi9mHDhuGzzz7DoUOHcObMGezatQtPP/00OncWz81Zt24d/vnPf3qkVnJO4dla3PbfbSivNSKlWzj+M3MIAjUqucsiIh/w12GJWDh1IBQK4NPthRj/+mZsyz8jd1nkpdo1FJiRkYGJEyeK2saMGYN169YhPDy82esNBgNmzJiBNWvWiNqXLFmC+++/X2oZDluwYAEWL14savvHP/6BRYsWtdi1e+LECYwfPx779u2ztSkUCvz5559ISUmRVAOHAl3vZFU9pi3PxrEztbgoOhSf35OGTiEBcpdFRD7ml7wyPP7lXpRVGQAAtw1PxKPj+0IX2L4pItQxOPr+LTlYCYKAwYMHY/fu3ba2xMRE5OTkQKfTtXqe2WzGsGHDsHPnTltbbGwsjhw5guDgYCmlOKSoqAi9e/dGfX29re0vf/kLvvrqqwueV1hYiP79+0Ov19vaJk6ciHXr1kmqg8HKtSpqG3DT8q04UKZHQqcgrLlnBGLDA+Uui4h8VFW9ES99ux+fbm98QCY+PBAvTB6IK/pGy1wZuZvb51itW7dOFKoA4I033rhgqAIan7RbsWKFqK20tLRZm6v93//9nyhUBQUFYenSpW2el5CQgGeffVbUtn79+mZ/d/K8aoMJM1f+jgNlekTrtPj4ruEMVUTkVmGBGrw0JQWfzB6GxE7BKK6sxx2rfsc/Pv8TZ2sa5C6PvIDkYLV69WrRcY8ePTBp0iSHzh08eHCz5QvshwddSRCEZtefNm0a4uLiHDr/zjvvREhIiKjNnfVS2+qNZtz9wQ7sLqxARLAGH941DImd3dfjSUR0vhG9o/D93y/H7FE9oVQAX+0qwlWLM7E88wgqahmw/JmkYGU0GvHtt9+K2pxdl2rWrFmi4+zsbJw8eVJKOW3asWMHiouLRW0zZ850+PywsDBMnjxZ1CZ1KJCkEwQBfxZW4N/r92Hky79gy5EzCAlQ4f07LsPFsRfuKSUicrWgABX+dX0/rL13BPrEhOJMTQNe+i4Pw1/6GY9/uQd5pVVyl0gykBSssrOzUVlZKWpLT0936hr2r7dYLNiwYYOUctr03XffiY4DAgIwYsQIp65hX29OTg6OHz/e3tLIAcfO1OCNnw7hykWZ+MvbWVi15SjO1DQgKlSL/8wcikEJEXKXSER+bHBiJL55YDRenjIQfWN1qDdamp4e/BW3vLsV3+8rhdnCbXH8haSV1/fs2SM6VqlUGDZsmFPXSEpKQkxMDMrKymxte/fulVJOm+zrvfTSSxEY6NxcnJEjRzZr27t3LxITE9tVm6vMX/0n9pecm2Df0jMJCoUCSgWgUipsv1cqFFApFIC1s1Gw/iLA/hIKReM1FE2/VyoUtl8DVEoEqJXQNP0aoFYiQKWEtun3WrUSWrXq3O81jcdqpQJGswCj2YIGswVGswVGkwVGs4DaBjMyD57EH8crbDUEapS4pn8s/jK4K0b1joJGxe0uiUh+AWolbr4sETcNTcD2grNYteUofsgtQ3b+GWTnn0HXiCDcmNoNaUmdcUlCBJeD8WGSglVeXp7oODExsdkcJEckJyeLgpX9dV3F/rr262k54qKLLoJKpYLZfG5huLy8PFx33XXtrs8Vjp6uwf4S3+x2ViqAkb2jMHlwV1zdPxahWrdscUlE1G4KhQLDenXGsF6dUVRRh4+2HsNn24+jqKIOb/x8CG/8fAgBKiUGJYRjWM/OuKxnJ6R2j0QIv6/5DEn/k/v37xcdS+21SUhIEB27I1iZzWYcOnRI1CalXpVKhfj4eBQWFtra3BUEpXjy+n7Q159baNU63U2Bc/PeBAgwWxp7oiyCAIuApuPG3587B82uIwiNnVnWcxt/33gts6Wxx8lotsBgaup5MgloMJvRYLKgwdTYbrD93tx4bLTAaLE09nKplNCoFNColNColdCqGnu/+sTqcMOgOETr+LQfEXUsXSOC8Oj4vnhw7EX4Zk8JNh04iW0FZ3FKb8DvR8vx+9FyYGPjKEL/+DB07xyC2DAtYsODEBceiJiwQMSFByJap4WavfMdhqRgZT8R3D4gOco+4BQVFUm6zoWcPn0aDQ3iJzTaU+/5wcod9Uo1ODFS7hKIiKgFgRoVbkzthhtTu0EQBBw7U4ttBWewreAstuWfRVFFHfacqMSeE5Utnq9UAGFBGmjVSgRqVLZfA9WqpmkVSoh/JBbP4zj/B2KLcO6H4tamfADnfii3TgFR2aZ/nJtGolQCKqUSGqUCapWi8fcqBdRKJdQqBTQqBbRq1XlTQlSi6SGBGlXTx7m/T2DAud9rVIoOuS+jpGBVXV0tOpa60KX9eXV1dbBYLFAqXZfM7Wtt6b6Osj+vpWsTERG1RqFQoEdUCHpEheCmoY2dCyfKa7G7sBIllXUoqaxHaVU9SisbP8qq6mGyCKioNcpcuecpFYBW3Ri87H8NOC+sBaga5+1a5/pq1SrcMbIHEjrJswSPS4JVUFCQpJu3dF51dbVLVyRvKfy4ql5HgpXBYIDBYLAdV1X55jwoIiKSpltkMLpFthwCLBYBp2sMqKozot7YOJWi3mhBvbFxSoX11/NZ+3jO7+xp7Glq7IdSKht7pKy9UUDzB56sh9ZpIxbh3LQRW5tFgMkiwGxpfODIZG76vaVx2klDC1NArL9v/LtYYDCaUWc0o97Y9Pcymc+7N1DX9OeAc8HyhkFxHStY1dTUiI6dfcLOyhPByr5WwHX1OhKsXnrpJTzzzDOS7kdERP5NqVQgWhfoN/NMBUFAg9mC+oZzYcw+RNYbzS2GNsN5c3rl3IWDjyG42eOPP4758+fbjquqqiTP8SIiIvJlCoV1XpYKQMfc3FpSsAoJCUFFRYXt+Pw9+JxRV1fXrC00NFTStVrT0jIQrqrXkVq1Wi20Wq2k+xEREVHHImmWuH2gaCkgOcITwaql67mqXlfXSkRERB2bS4KV1AnZ9ucFBQW59IlAoOXw46p6GayIiIjofJJSTHx8vOj4/LWdnGF/nv11XSEqKgoBAQEXvK+jPFEvERERdVySglXfvn1Fx1I3I7Y/T8pWM21RqVTo3bv3Be/rCLPZ3GxhVHfUS0RERB2XpGBlHygKCwtbXNagLfZbwtgHNlexr9d+Sx5HHD58GCaTSdTmrnqJiIioY5IUrFJSUkTHJpMJ27dvd+oaR44cQWlpqaht4MCBUsppk329f/zxh9NPBv7222/N2txVLxEREXVMkoJVWloawsPDRW2bNm1y6hqZmZniQpRKjB8/Xko5bZowYYLouKGhAdnZ2U5dw77eAQMGSN58moiIiHyTpGCl0Whw7bXXito+/PDDZkviX8j7778vOk5LS0N0dLSUcto0ZMiQZhPN7e9/IXq9Hl9++aWobdKkSS6pjYiIiHyH5LUNpk+fLjouKChARkaGQ+fu3r0bmzdvFrVNmzZNailtUigUza6/Zs2aZkORrVm5cmWzOWQ33nijy+ojIiIi3yA5WE2aNKnZ3KV58+a1uX+e2WzG7NmzRW2xsbGYM2eOQ/dVKBSij/T0dIfOe/jhh0V7BNbW1uL+++9v87wTJ07gySefFLVNnDgRl1xyiUP3JSIiIv8hOVgpFAo8//zzorZjx45h4sSJrS7A2dDQgFtvvRU7duwQtT/xxBMIDnbvLtRdu3bFfffdJ2pbu3YtHn744VaHME+cOIEJEyaI/j4KhQLPPfecW2slIiKijqldy5zfcMMNmDp1qqht48aNSElJwZIlS5Cbm4uKigocOHAA7733HgYPHozPP/9c9Pq0tDTMnTu3PWU47Omnn0avXr1Eba+++ipGjBiBNWvWID8/H+Xl5di9ezeeffZZDB48GDk5OaLXL1iwoFlPHREREREAKARnZpy3QK/XY9y4cU4vtwAASUlJ+PXXXxEXF+fwOQqFQnQ8ZswYp55I3LdvH8aMGYMzZ844fI7VxIkTsXbtWqjVkvauBgBUVlYiIiIChYWFCAsLk3wdIiIi8pyqqiokJCSgoqKi2coIIoILVFVVCZMnTxYAOPwxevRoobS01Ol72V9nzJgxTl8jLy9P6N+/v1P13nfffUJDQ4PT97JXWFjo1H35wQ9+8IMf/OCH93wUFhZe8H2+3T1W51u/fj0WLlyILVu2tPqalJQUzJ8/H7fffnuz3idHtLfHyspoNGLp0qV48803kZ+f3+JrlEolrr76avzzn//E6NGjnb5HSywWC4qLi6HT6ST9/VtjTdLsCSN/wM938if8fPcOgiBAr9cjPj4eSmXrM6lcGqysiouLsXXrVhw9ehQ1NTUICgpCQkIChg4d2myOkzfYt28fdu3aheLiYjQ0NECn0yEpKQlpaWno3Lmz3OU5pKqqCuHh4aisrOQXHvk8fr6TP+Hne8filmBFnscvPPIn/Hwnf8LP946lXU8FEhEREdE5DFY+QqvV4umnn4ZWq5W7FCK34+c7+RN+vncsHAokIiIichH2WBERERG5CIMVERERkYswWBERERG5iPS9WcgrlJSUYNu2bSgoKLCtGZaYmIghQ4agZ8+ecpdHZHPixAnk5OTg+PHjKC8vBwBERkYiJiYGQ4cORXx8vMwVEnV81dXVyM7OxsGDB1FRUQGNRoOYmBikpKTgkksucenC1NQyBqsOKiMjw7bKfWvPHwwaNAjz58/HjBkz+MVEHldWVob169fj559/xsaNG3Hy5MkLvr5Xr1646667cPfddyMqKsrp+6WnpyMzM1NquTZ6vR6hoaHtvg75F1d8j01NTcWOHTsknbt37148//zz+Prrr9HQ0NDia+Lj43HPPfdgwYIFCAkJaU+pdCHt3vyOPEqv1wtTp051al+jMWPGSNqXkUiK33//XbjiiisEpVIpaR8unU4n/Pe//3X6vmPGjHHJPmB6vd4N/yrk61zxuZeamur0fS0Wi/Dcc88JarXa4fskJiYK27Ztc8O/AgmCIHCOVQdSXV2NcePGYe3atU6dl5mZiVGjRqG0tNRNlRGds2PHDmzcuBEWi0XS+Xq9HnfddRfuvfdeF1dG5Hvuv/9+PPnkkzCZTA6fc/z4caSnp2Pz5s1urMx/cSiwA7nzzjuxbds2UVtiYiLmz5+PsWPHolu3bigpKUFWVhYWL16M/fv32153+PBhTJkyBZs3b4Zazf928qzevXvjqquuQnp6OpKTkxEdHY3AwECUlpZiy5YtePfdd7F161bROcuWLUN0dDSeeeYZSfe89tprMXDgQKfPCwgIkHQ/ovPNnTsX4eHhTp3TrVs3p17/9ttvY+nSpaK20NBQ3HPPPbjpppvQq1cv6PV67N27F0uWLMGPP/5oe11dXR0mT56MP//8EwkJCU7dl9ogd5cZOWb9+vUtDvFVVFS0+Pr6+nph2rRpzc5ZsmSJhysnf/POO+8IAAS1Wi3cdtttwqZNmxw6b/ny5YJGoxF9vqrVaiEnJ8eh8+2HAleuXNmOvwWRc+y/1xYUFLj1fqWlpUJwcLDonl27dr3g18urr74qKBQK0TlTp051a53+iEOBHYAgCHjyySdFbYmJicjIyGj1JyKtVotPP/0UqampovYXXngBtbW1bquVSKlU4pZbbsH+/fvx4YcfYsyYMQ6dd/fdd2P58uWiNpPJhGeffdYdZRJ1aPbfy1UqFdatW4f+/fu3es6CBQswb948UduXX36JP/74w211+iMGqw5g3bp12L17t6jtjTfegE6nu+B5KpUKK1asELWVlpY2ayNypbvuuguffPIJevfu7fS5d9xxR7Mg9t1337X6lBORPyorK2v2Q8j999/f7AfplrzwwguipU0EQcBzzz3n8hr9GYNVB7B69WrRcY8ePTBp0iSHzh08eDBGjRolaluzZo3LaiOyp1Kp2nX+zJkzRcd6vR579uxp1zWJfMm6detEP2woFIpmPVGtCQkJwZ133ilq++6771BdXe3SGv0Zg5WXMxqN+Pbbb0Vtzq5LNWvWLNFxdnZ2m2sKEcll0KBBzdpKSkpkqITIO61bt050PGrUKPTq1cvh8+3fEwwGA77//ntXlEZgsPJ62dnZqKysFLWlp6c7dQ3711ssFmzYsKGdlRG5R1BQULO2mpoaGSoh8j4NDQ34+eefRW3OvickJSU1ewLxf//7X3tLoyYMVl7OfghEpVJh2LBhTl0jKSkJMTExora9e/e2uzYidzh+/HiztujoaBkqIfI+Bw8ehMFgELWNHDnS6evYn8P3BNfhgkZeLi8vT3ScmJgoaSuC5ORklJWVtXpdIm/R0rY0SUlJTl9HEATk5ORgx44dKCsrQ01NDSIjI9G5c2ckJyfj0ksvbfd8MKLWGAwGZGVlIScnB6dOnYLZbEbnzp0RFRWF1NRUXHTRRZKu29L37uTkZKevY3/OgQMHJNVDzTFYebnzF/kEGoOVFPYLwDFYkTcym8346KOPRG3Jycno3r2709e6++67L7gatU6nwzXXXIOHHnrI6V5gorYMGDDggp9/cXFxmDZtGhYsWODU93X79wSVSoWuXbs6XZ/9e4Jer0dRUZGka5EYhwK9XHFxsehY6gq59l+4RUVFkmsicpf//Oc/KCwsFLXddNNNkq7V1hYfer0eX3zxBYYPH44bbrgBp0+flnQfopa09flXUlKCN998E71798Zjjz3m8JY09u8JcXFxknpeWwpzfF9wDQYrL2f/CGxYWJik69ifV1dXJ3kvNyJ3OHHiBB599FFRW2RkJB544AG33/ubb77B4MGDm60XR+RuRqMRCxcuxJVXXtnsQaWWuOs9oaVrkzQMVl7O/hO9pSemHNHSefwiIm9hMpnw17/+tdkby4svvohOnTo5da2LLroI8+fPxxdffIG8vDyUl5fDaDTizJkz2Lt3L5YtW4Yrrrii2XknTpzAdddd16xHgMhRSqUSw4cPx3PPPYfvvvsOR48ehV6vR0NDA8rKypCdnY2XX365xflVv/76K6ZNm9ZmzxXfEzoAmbfUoTbY7532xBNPSLrOihUrmu1lVVRU5OJqiaT529/+1uzz85prrhEsFovD11i0aJGQlZXl8Os3btwodO3atdl9R48eLeWvQH7u4YcfFvLz8x16rdlsFt5++21Bq9U2+/x78sknL3juVVddJXr9yJEjJdV76NChZvf++OOPJV2LxNhjRUSyWrJkCd5++21RW7du3fDhhx86tRDu/PnzMWLECIdfn56ejt9//73ZXJNff/2Va/qQ01555RX07NnTodcqlUrcd999+O6776DRaER/tnjxYi7g3MExWHk5+6UV6uvrJV2nrq6uWVtoaKikaxG5ymeffYa///3vorbw8HD873//Q5cuXdx+/7i4OHzxxRfNAtyiRYvcfm+iK664Ai+++KKoraamBsuWLWv1HL4neD8GKy9n/4ne0heDI/hFRN5mw4YNuP3220UPUQQFBSEjIwMpKSkeq2Po0KGYPHmyqC0rK4urvZNHPPDAA6JNkQFccGcMvid4PwYrL2f/iV5VVSXpOvbnBQUFQankfz/J47fffsPUqVNhNBptbRqNBmvWrMHo0aM9Xs+NN94oOm5oaMCWLVs8Xgf5H61Wi+uvv17Utn379lYDk7veE1q6NknDd1YvZ/+TjP0aP46yP8/+ukSesnPnTlx33XWora21tSmVSnzwwQe47rrrZKkpLS2tWRvX9CFPsf/8M5vNop0yzmf/vbukpARms9npe7b0XsL3BddgsPJyffv2FR23tI+aI+zPk7IFAlF77du3D9dcc02zn5aXLVuGm2++WaaqWt6L8NSpUzJUQv7Imc8/+/cEs9ks6YcA+/cEnU7XbGNmkobBysvZB6DCwkJJcz/st7Cx/+IkcrfDhw/jqquuwpkzZ0TtixYtwpw5c2SqqpEgCM3anHkikag9nPn8a+mHYvttbhxh/55w8cUXO30NahmDlZezn8RrMpmwfft2p65x5MgRlJaWitoGDhzY7tqIHFVYWIhx48ahpKRE1P7vf/8b8+fPl6mqc1oadvHEU4lEgHOff3369IFWqxW1SZkPmJWVJTrme4LrMFh5ubS0NISHh4vaNm3a5NQ1MjMzRcdKpRLjx49vb2lEDikrK8PYsWNx7NgxUfuCBQvw9NNPy1SVWHZ2drO2uLg4GSohf2T/+adSqVocHgSAgIAAjB07VtTm7HtCfn5+szlWcs1v9EUMVl5Oo9Hg2muvFbV9+OGHLXYdt+b9998XHaelpbX6RUvkSmfPnsVVV12FQ4cOidrvuecevPrqqzJV1dzq1atFxxqNpsUJ7USuVldXh2+++UbUNnTo0AtuVTNp0iTR8a+//or8/HyH72n/nqDVanHNNdc4fD5dGINVBzB9+nTRcUFBATIyMhw6d/fu3di8ebOobdq0aS6rjag11dXVmDBhAvbu3Stqv+222/DOO+/IVFVz2dnZWL9+vaht5MiR0Ol0MlVE/uT1119vNlWjrRGFSZMmISAgwHYsCALeeusth+5XW1uL//73v6K2CRMmcKkFV5J3Rx1yhMViEVJSUkR7OnXv3l3Q6/UXPM9kMglDhgwRnRcbGyvU1NR4qHLyV3V1dUJ6enqzvcimTJkimEwml96rqqpKqKyslHTu8ePHhfj4+GZ1rl+/3qU1ku8qKSmR/Dn9/fffC2q1WvS5FxwcLJSUlLR57v333y86T61WC3/88Ueb5/3jH/8QnadQKISdO3dKqp9axmDVQaxfv77ZN/8rrrii1TcUg8Eg3HTTTc3OWbJkiYcrJ39jNBqF66+/vtnn3oQJEwSDweDy++3atUuIjIwUnnnmGac2Ft+wYYMQExPTrM5Ro0a5vEbyXa+99prQp08f4b333mvzh10ro9EoLF68WNBoNM0+//71r385dI2SkhIhODhYdG5CQoKwb9++Vs9ZvHixoFAoROdMnTrVofuR4xSC4MRkHZLVjTfeiLVr14raunfvjgULFmDs2LGIj49HWVkZsrKysGjRIuTm5opem5aWhs2bN0OtVnuybPIzTzzxRLP9z9RqNe67774Lzhu5kNTU1FaHsP/8808MHjwYQOODGSNHjsTYsWMxePBg9OnTBxEREQgNDYVer0dxcTGysrLw6aefYuvWrc2u1a1bN2zduhVdu3aVVCf5n9dffx3/+Mc/ADTuaDF+/HiMHDkSgwcPRvfu3REeHo7AwECUl5cjPz8fmzZtwsqVK5s9zAEAV199Nb755ptmGzO35q233sIDDzwgatPpdJg7dy5uuukm9OzZE9XV1dizZw+WLFmCH374QfTaTp06YdeuXc02Iqd2kjvZkeOqqqqEyy67rNlPOI58JCUlCcXFxXL/FcgPzJw5U9Ln6IU+Zs6c2er9du3a5ZJ79OjRQ8jJyfHcPxT5hNdee80ln39XXnmlUFVV5fT97733Xkn3CwoKEjZt2uSGfxHi5PUORKfT4aeffmq2YWxbRo8ejaysLD4+TtQChUKB22+/Hbt370b//v3lLof8THBwMBYuXIgff/xR0gMTb7/9Np555hmoVCqHz0lISMDGjRsxZswYp+9HbWOw6mB0Oh2+/PJLrFu3DiNGjLjga1NSUrBq1SpkZmYiJibGQxUSedbFF1+MTz75BHPmzEH//v0dfoPp3r07HnzwQRw8eBDvv/8+wsLC3Fwp+aKbbroJS5cuxbRp09C9e3eHzlEqlRg0aBBeeeUVnDhxAo888giUSmlvxwqFAk899RT++OMPTJs2TfS0oL24uDj8+9//Rm5uLoYNGybpftQ2zrHq4IqLi7F161YcPXoUNTU1CAoKQkJCAoYOHYpevXrJXR6Rx9XX1+PAgQM4duwYiouLodfrUV9fj5CQEERGRqJLly5ITU1lDy65RUVFBfLy8lBYWIjS0lLU1NTAZDIhLCwMkZGR6NatG4YMGeK25Tz0ej22bNmCQ4cOobKyEiqVCjExMRg0aBAGDx7MrZo8gMGKiIiIyEU4FEhERETkIgxWRERERC7CYEVERETkIgxWRERERC7CYEVERETkIgxWRERERC7CYEVERETkIgxWRERERC7CYEVERETkIgxWRERERC7CYEVERETkIgxWRERERC7CYEVERETkIgxWRERERC7CYEVERETkIgxWRERERC7CYEVERETkIgxWRERERC7y//CeDWDd29P4AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "start_col = '60'\n",
        "end_col = '120'\n",
        "cols_drop = [str(i) for i in range(0, int(start_col))] + [str(i) for i in range(int(end_col)+1, (int(df.columns[-1]) + 1))]\n",
        "df = df.drop(cols_drop, axis=1)\n",
        "df.columns\n",
        "for index, symbol in enumerate(all_types):\n",
        "  new_df = df[df[symbol]==1]\n",
        "  index = randint(0, new_df.shape[0]-1)\n",
        "  y =  new_df.iloc[index][start_col : df.columns[-1]].to_numpy()\n",
        "\n",
        "  plt.plot(y)\n",
        "  plt.title(label=\"%s beat\"%symbol)\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1B0UATgm4SM"
      },
      "source": [
        "\n",
        "# Preparing data for training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a_Bs8YK7efgn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92277380-b25a-483f-8dfc-dae1d555d38c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.12.0\n",
            "[name: \"/device:CPU:0\"\n",
            "device_type: \"CPU\"\n",
            "memory_limit: 268435456\n",
            "locality {\n",
            "}\n",
            "incarnation: 11453467716290394613\n",
            "xla_global_id: -1\n",
            ", name: \"/device:GPU:0\"\n",
            "device_type: \"GPU\"\n",
            "memory_limit: 14328594432\n",
            "locality {\n",
            "  bus_id: 1\n",
            "  links {\n",
            "  }\n",
            "}\n",
            "incarnation: 7752527202490255242\n",
            "physical_device_desc: \"device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\"\n",
            "xla_global_id: 416903419\n",
            "]\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "from tensorflow.python.client import device_lib\n",
        "print(device_lib.list_local_devices())\n",
        "from tensorflow.keras.layers import Dense, Conv1D\n",
        "from tensorflow.keras import Model, Sequential, models\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "features =  df.loc[:, start_col : end_col].to_numpy()\n",
        "labels =  np.array(df[all_types])\n",
        "x_train, x_test, y_train, y_test = train_test_split(features,\n",
        "                                                    labels,\n",
        "                                                    test_size=0.33,\n",
        "                                                    stratify=labels,\n",
        "                                                    random_state=42)\n",
        "\n",
        "x_train = np.expand_dims(x_train, axis=1).astype(\"float32\")\n",
        "x_test = np.expand_dims(x_test, axis=1).astype(\"float32\")\n",
        "y_train = np.expand_dims(y_train, axis=1).astype(\"float32\")\n",
        "y_test = np.expand_dims(y_test, axis=1).astype(\"float32\")\n",
        "\n",
        "\n",
        "# x_train = x_train.astype(\"float32\")\n",
        "# y_train = y_train.astype(\"float32\")\n",
        "\n",
        "batch = 1024\n",
        "\n",
        "train_ds = tf.data.Dataset.from_tensor_slices(\n",
        "    (x_train, y_train)).shuffle(10000).batch(batch)\n",
        "\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XrV2ZfQNmCct"
      },
      "source": [
        "# Designing the neural network\n",
        "\n",
        "* Adam + MSE + last layer relu causes stagnation - for 1000 epochs and 1024 batch size\n",
        "* SGD + MSE +last layer relu causes stagnation  - for 1000 epochs and 1024 batch size\n",
        "\n",
        "* Adam + CE + last layer relu causes loss=nan\n",
        "* SGD + CE +last layer relu causes loss=nan\n",
        "\n",
        "* Adam + MSE + last layer sigmoid causes accuracy = 93.297290802001953125% - for 1000 epochs. For 10,000 epochs it gives 93.44364929199219% accuracy. Batch size was 1024.\n",
        "* SGD + MSE + last layer sigmoid causes stagnation\n",
        "\n",
        "* Adam + CE + last layer sigmoid causes accuracy = 92.983663082122802734375% - for 1000 epochs and 1024 batch size\n",
        "\n",
        "* SGD + CE + last layer sigmoid causes stagnation - for 1000 epochs and 1024 batch size\n",
        "\n",
        "#2+ layer\n",
        "* For 1st layer 1, 4, 4 accuracy 93.4346923828125%\n",
        "* For 1st layer 4 all sigmoid accuracy 96.0721664428711%\n",
        "* For 1st layer 8 all sigmoid accuracy 96.6516342163086%\n",
        "* For 1st layer 9 all sigmoid accuracy approx  97.00408935546875%\n",
        "* For 1st layer 10 all sigmoid accuracy approx 97.20720672607422%\n",
        "* For 1st layer 12 all sigmoid accuracy 97.25798034667969%"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "fSnnmhOnFYUq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ZPNry2Gmqiy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4955acb0-fc17-4a56-cffb-684a887f1447"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
            "\n",
            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
            "\n",
            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "import tensorflow_addons as tfa\n",
        "\n",
        "MODEL_DIR = \"%s/model.h5\" % (PATH)\n",
        "\n",
        "\n",
        "# input_samp, output_samp = next(train_ds.as_numpy_iterator())\n",
        "input_samp, output_samp = None, None\n",
        "\n",
        "model = Sequential([\n",
        "           Dense(10, activation='sigmoid'), #14\n",
        "           Dense(len(all_types), activation='softmax')\n",
        "        ])\n",
        "\n",
        "loss_object = tf.keras.losses.MeanSquaredError() # Try catergorical cross entropy\n",
        "optimizer = tf.keras.optimizers.Adam()\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss=loss_object,\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "train_loss = tf.keras.metrics.MeanSquaredError(name='train_loss')\n",
        "train_accuracy = tf.metrics.CategoricalAccuracy(name=\"train_accuracy\")\n",
        "train_f1score = tfa.metrics.F1Score(name=\"train_f1score\", num_classes=4, average='macro')\n",
        "\n",
        "\n",
        "test_loss = tf.keras.metrics.MeanSquaredError(name='test_loss')\n",
        "test_accuracy = tf.keras.metrics.CategoricalAccuracy(name='test_accuracy')\n",
        "test_f1score = tfa.metrics.F1Score(name=\"test_f1score\", num_classes=4, average='macro')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBHCJQmgmwQ3"
      },
      "source": [
        "# Training the network\n",
        "\n",
        "* Quantization eqn, $\\begin{align} x_q = \\text{round}\\big(\\frac{1}{s} x - z\\big) \\\\ \\end{align}$\n",
        "* Dequantization eqn, $\\begin{align} x = s (x_q + z) \\\\ \\end{align}$\n",
        "\n",
        "### Finding scale and zero via system of two equations:\n",
        "* scale, $\\begin{align} s &= \\frac{\\beta - \\alpha}{\\beta_q - \\alpha_q} \\end{align}$\n",
        "* zero, $\\begin{align}z &= \\frac{\\alpha \\beta_q - \\beta \\alpha_q}{\\beta - \\alpha} \\end{align}$\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qo7TQUEo9PsB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61cd6030-afaf-46de-f926-dadab491356d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, Loss: 0.1442273110151291, Accuracy: 55.55375671386719, F1-score: 19.59364891052246, Test Loss: 0.09750189632177353, Test Accuracy: 89.33361053466797 Test F1-score: 23.591588973999023, Time: 21.33483076095581 seconds\n"
          ]
        }
      ],
      "source": [
        "@tf.function\n",
        "def train_step(features, labels):\n",
        "  with tf.GradientTape() as tape:\n",
        "    # training=True is only needed if there are layers with different\n",
        "    # behavior during training versus inference (e.g. Dropout).\n",
        "    predictions = model(features)\n",
        "    loss = loss_object(labels, predictions)\n",
        "  gradients = tape.gradient(loss, model.trainable_variables)\n",
        "  optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
        "\n",
        "  train_loss(labels, predictions)\n",
        "  train_accuracy(labels, predictions)\n",
        "  train_f1score(tf.squeeze(labels), tf.squeeze(predictions))\n",
        "\n",
        "\n",
        "@tf.function\n",
        "def test_step(features, labels):\n",
        "  # training=False is only needed if there are layers with different\n",
        "  # behavior during training versus inference (e.g. Dropout).\n",
        "  predictions = model(features, training=False)\n",
        "  t_loss = loss_object(labels, predictions)\n",
        "\n",
        "  test_loss(labels, predictions)\n",
        "  test_accuracy(labels, predictions)\n",
        "  test_f1score(tf.squeeze(labels), tf.squeeze(predictions))\n",
        "\n",
        "\n",
        "def save_model(model):\n",
        "  model.save(MODEL_DIR)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "EPOCHS = 20000\n",
        "\n",
        "test_f1_prev = -np.inf\n",
        "test_losses = []\n",
        "\n",
        "for epoch in range(0, EPOCHS):\n",
        "  # Reset the metrics at the start of the next epoch\n",
        "  start_time = time()\n",
        "  train_loss.reset_states()\n",
        "  train_accuracy.reset_states()\n",
        "  train_f1score.reset_states()\n",
        "\n",
        "  test_loss.reset_states()\n",
        "  test_accuracy.reset_states()\n",
        "  test_f1score.reset_states()\n",
        "\n",
        "\n",
        "  for x, y in train_ds:\n",
        "    train_step(x, y)\n",
        "\n",
        "  for x, y in test_ds:\n",
        "    test_step(x, y)\n",
        "\n",
        "  #Saving the best model\n",
        "  if test_f1_prev < test_f1score.result():\n",
        "    save_model(model)\n",
        "    test_f1_prev = test_f1score.result()\n",
        "\n",
        "  test_losses.append(test_loss.result())\n",
        "  print(\n",
        "      f'Epoch {epoch + 1}, '\n",
        "      f'Loss: {train_loss.result()}, '\n",
        "      f'Accuracy: {train_accuracy.result() * 100}, '\n",
        "      f'F1-score: {train_f1score.result() * 100}, '\n",
        "      f'Test Loss: {test_loss.result()}, '\n",
        "      f'Test Accuracy: {test_accuracy.result() * 100} '\n",
        "      f'Test F1-score: {test_f1score.result() * 100}, '\n",
        "      f'Time: {time() - start_time} seconds'\n",
        "    )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2QSydRFW4Ld5"
      },
      "source": [
        "# Load the best model and evaluate\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xw0TbLPF3u_Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "outputId": "3289735b-a01e-4a7f-d468-ae78e641ce64"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33/33 [==============================] - 0s 7ms/step - loss: 0.0975 - accuracy: 0.8933\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 1, 10)             620       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1, 4)              44        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 664\n",
            "Trainable params: 664\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = models.load_model(MODEL_DIR)\n",
        "test = model.evaluate(test_ds)\n",
        "# print('Restored model, accuracy: %.60g'%(100 * acc), \"%\")\n",
        "model.summary()\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (16,9)\n",
        "plt.plot(test_losses, linewidth=6)\n",
        "plt.savefig('/content/sigmoid_plus_sigmoid.png')\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "# Upload pretrained model\n"
      ],
      "metadata": {
        "id": "ogWryj9XVEme"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import files\n",
        "# uploaded = files.upload()"
      ],
      "metadata": {
        "id": "wDzxzIYqVCwG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "a5af1382-fd32-442f-dc55-4fb0f58b36d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8eb3eb7f-86e5-4cc9-b914-d5a062220a2f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8eb3eb7f-86e5-4cc9-b914-d5a062220a2f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving model.h5 to model (1).h5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load model, scale and zero"
      ],
      "metadata": {
        "id": "crzWMIgdFzZG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8BhKWQ6UTWce"
      },
      "outputs": [],
      "source": [
        "def quantize(x, scale, zero, alpha_q, beta_q):\n",
        "  x =  round((x/scale) - zero)\n",
        "  if x < alpha_q:\n",
        "    return alpha_q\n",
        "  elif x > beta_q:\n",
        "    return beta_q\n",
        "  return x\n",
        "\n",
        "def check_model(model):\n",
        "  max = -np.inf\n",
        "  min = np.inf\n",
        "  for i, v in enumerate(model.trainable_variables):\n",
        "    nmax = np.amax(model.trainable_variables[i].numpy())\n",
        "    max = max if max > nmax else nmax\n",
        "\n",
        "    nmin = np.amin(model.trainable_variables[i].numpy())\n",
        "    min = min if min < nmin else nmin\n",
        "\n",
        "  alpha = -max\n",
        "  beta = max\n",
        "\n",
        "  alpha_q = -127\n",
        "  beta_q = 127\n",
        "\n",
        "  scale = (beta - alpha)/(beta_q - alpha_q)\n",
        "  zero = (alpha * beta_q - beta * alpha_q) / (beta - alpha)\n",
        "\n",
        "  # quantize = lambda x, scale=scale, zero=zero, alpha_q=alpha_q, beta_q=beta_q : round((x/scale) - zero) if x >= alpha_q and x <= beta_q else (alpha_q if x < alpha_q else beta_q)\n",
        "  dequantize = lambda x_q, scale=scale, zero=zero: scale * (x_q + zero)\n",
        "\n",
        "  weight_sample = []\n",
        "  rindices = []\n",
        "\n",
        "\n",
        "  for i, v in enumerate(model.trainable_variables):\n",
        "    arr = model.trainable_variables[i].numpy()\n",
        "    randomi = np.random.randint(0, len(arr.flatten()))\n",
        "    rindices.append(randomi)\n",
        "    weight_sample.append(arr.flatten()[randomi])\n",
        "\n",
        "  loss, acc = model.evaluate(test_ds)\n",
        "  print('Regular model, accuracy: %.60g'%(100 * acc), \"%\")\n",
        "  print('Weight samples ', weight_sample)\n",
        "\n",
        "  weight_sample = []\n",
        "\n",
        "  for i, v in enumerate(model.trainable_variables):\n",
        "    arr = model.trainable_variables[i].numpy()\n",
        "    arr = np.vectorize(quantize)(arr, scale, zero, alpha_q, beta_q)\n",
        "    weight_sample.append(arr.flatten()[rindices[i]])\n",
        "    model.trainable_variables[i].assign(arr)\n",
        "\n",
        "  loss, acc = model.evaluate(test_ds)\n",
        "  print('Quantized model, accuracy: %.60g'%(100 * acc), \"%\")\n",
        "  print('Weight samples ', weight_sample)\n",
        "\n",
        "\n",
        "\n",
        "  weight_sample = []\n",
        "\n",
        "  for i, v in enumerate(model.trainable_variables):\n",
        "    arr = model.trainable_variables[i].numpy()\n",
        "    arr = np.vectorize(dequantize)(arr)\n",
        "    weight_sample.append(arr.flatten()[rindices[i]])\n",
        "    model.trainable_variables[i].assign(arr)\n",
        "\n",
        "  loss, acc = model.evaluate(test_ds)\n",
        "  print('Dequantized model, accuracy: %.60g'%(100 * acc), \"%\")\n",
        "  print('Weight samples = ', weight_sample)\n",
        "\n",
        "\n",
        "  return scale, zero, alpha_q, beta_q, alpha, beta\n",
        "\n",
        "\n",
        "MODEL_DIR = f'model.h5'\n",
        "model = models.load_model(MODEL_DIR)\n",
        "scale, zero, alpha_q, beta_q, alpha, beta = check_model(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJx9A-sxaQjf"
      },
      "source": [
        "# Calculating weight and bias size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WDRje2y_0AWC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73114a6c-a703-4318-b539-9f5d21e4fda9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "for layer 0, kernel_bytes = 2440, and bias_bytes = 40\n",
            "for layer 1, kernel_bytes = 160, and bias_bytes = 16\n",
            "Total bytes= 2656\n"
          ]
        }
      ],
      "source": [
        "tbytes = 0\n",
        "for i, l in enumerate(model.layers):\n",
        "  kernel = l.weights[0].numpy()\n",
        "  bias = l.weights[1].numpy()\n",
        "  tbytes += kernel.nbytes\n",
        "  tbytes += bias.nbytes\n",
        "  print(\"for layer %d, kernel_bytes = %d, and bias_bytes = %d\"% (i, kernel.nbytes, bias.nbytes))\n",
        "\n",
        "\n",
        "print(\"Total bytes= %d\"%tbytes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJlbRfKpo9Rl"
      },
      "source": [
        "# Writing basic DNN methods for cpp implementation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VKxWpTRWXuZT"
      },
      "outputs": [],
      "source": [
        "def matmul(mat1, mat2):\n",
        "  r1= mat1.shape[0]\n",
        "  c1= mat1.shape[1]\n",
        "  r2= mat2.shape[0]\n",
        "  c2= mat2.shape[1]\n",
        "  assert c1 == r2\n",
        "  res = np.zeros(shape=(r1, c2))\n",
        "  for i in range(r1):\n",
        "    for j in range(c2):\n",
        "      for k in range(c1):\n",
        "        res[i,j]+= mat1[i, k] * mat2[k, j]\n",
        "  return res\n",
        "\n",
        "def relu(x):\n",
        "  return np.maximum(0,x)\n",
        "\n",
        "def softmax(x):\n",
        "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
        "    e_x = np.exp(x - np.max(x))\n",
        "    return e_x / e_x.sum()\n",
        "\n",
        "def sigmoid(x):\n",
        "  return 1/(1 + np.exp(-x))\n",
        "\n",
        "def dense(input, kernel, bias, func):\n",
        "  if len(input.shape) < 2:\n",
        "    input = np.array([input])\n",
        "  return func(matmul(input, kernel) + bias)\n",
        "\n",
        "corr = 0\n",
        "labels = y_test\n",
        "features = x_test\n",
        "kernels = [l.weights[0].numpy() for l in model.layers]\n",
        "biases = [l.weights[1].numpy() for l in model.layers]\n",
        "for (i, y) in enumerate(labels):\n",
        "  x = dense(features[i], kernels[0], biases[0], sigmoid)\n",
        "  x = dense(x, kernels[1], biases[1], softmax)\n",
        "  if np.argmax(y) == np.argmax(x):\n",
        "    corr += 1\n",
        "print(\"Validation accuracy %.60g\"%((corr/len(labels))*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Input sample selection"
      ],
      "metadata": {
        "id": "MVLj3jloypS4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('%s/mit_bih.csv'%(PATH))\n",
        "\n",
        "\n",
        "for i, symbol in enumerate(all_types):\n",
        "  new_df = df[df[symbol]==1]\n",
        "  index = randint(0, new_df.shape[0]-1)\n",
        "\n",
        "  if i== 0:\n",
        "    input_samps =  eval((new_df.iloc[index]['beat']))\n",
        "\n",
        "  else :\n",
        "    input_samps = np.vstack([input_samps, eval((new_df.iloc[index]['beat']))])\n",
        "\n",
        "input_samps"
      ],
      "metadata": {
        "id": "0MCbv7sRyo4P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cbeeae8-0785-4285-a619-46091f98ed62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.   ,  0.   ,  0.   , -0.24 , -0.215, -0.17 , -0.165, -0.155,\n",
              "        -0.14 , -0.11 , -0.13 , -0.16 , -0.17 , -0.19 , -0.215, -0.215,\n",
              "        -0.235, -0.28 , -0.32 , -0.37 , -0.385, -0.42 , -0.435, -0.485,\n",
              "        -0.5  , -0.505, -0.515, -0.525, -0.535, -0.555, -0.55 , -0.525,\n",
              "        -0.52 , -0.495, -0.5  , -0.44 , -0.405, -0.325, -0.3  , -0.275,\n",
              "        -0.26 , -0.23 , -0.215, -0.22 , -0.23 , -0.315, -0.335, -0.415,\n",
              "        -0.47 , -0.525, -0.565, -0.605, -0.605, -0.595, -0.585, -0.585,\n",
              "        -0.585, -0.58 , -0.585, -0.6  , -0.61 , -0.6  , -0.61 , -0.59 ,\n",
              "        -0.58 , -0.58 , -0.6  , -0.65 , -0.625, -0.385, -0.17 ,  0.45 ,\n",
              "         0.83 ,  1.61 ,  1.91 ,  1.99 ,  1.765,  0.975,  0.54 , -0.23 ,\n",
              "        -0.505, -0.97 , -1.125, -1.19 , -1.09 , -0.865, -0.78 , -0.77 ,\n",
              "        -0.8  , -0.875, -0.9  , -0.9  , -0.885, -0.905, -0.905, -0.93 ,\n",
              "        -0.93 , -0.92 , -0.935, -0.955, -0.98 , -0.995, -0.985, -0.97 ,\n",
              "        -0.955, -0.96 , -0.965, -0.975, -0.98 , -0.945, -0.915, -0.9  ,\n",
              "        -0.885, -0.845, -0.82 , -0.775, -0.765, -0.735, -0.7  , -0.61 ,\n",
              "        -0.56 , -0.48 , -0.445, -0.39 , -0.355, -0.28 , -0.235, -0.18 ,\n",
              "        -0.145, -0.05 , -0.01 ,  0.045,  0.075,  0.085,  0.08 ,  0.145,\n",
              "         0.165,  0.175,  0.165,  0.135,  0.115,  0.085,  0.075,  0.045,\n",
              "         0.03 , -0.005, -0.025,  0.   ,  0.   ,  0.   ],\n",
              "       [ 0.   ,  0.   ,  0.   , -0.12 , -0.125, -0.095, -0.075, -0.045,\n",
              "        -0.03 , -0.02 , -0.01 ,  0.02 ,  0.015,  0.025,  0.03 ,  0.05 ,\n",
              "         0.025,  0.   , -0.015, -0.025, -0.02 , -0.015, -0.005,  0.05 ,\n",
              "         0.045,  0.035,  0.03 ,  0.02 ,  0.02 , -0.01 , -0.035, -0.06 ,\n",
              "        -0.06 , -0.11 , -0.13 , -0.145, -0.16 , -0.195, -0.205, -0.2  ,\n",
              "        -0.21 , -0.235, -0.245, -0.235, -0.225, -0.25 , -0.255, -0.245,\n",
              "        -0.24 , -0.245, -0.26 , -0.24 , -0.235, -0.26 , -0.265, -0.245,\n",
              "        -0.24 , -0.245, -0.255, -0.24 , -0.235, -0.245, -0.255, -0.23 ,\n",
              "        -0.23 , -0.235, -0.225, -0.185, -0.16 , -0.1  , -0.045,  0.11 ,\n",
              "         0.17 ,  0.25 ,  0.245,  0.245,  0.21 ,  0.   , -0.055, -0.03 ,\n",
              "        -0.085, -0.29 , -0.345, -0.395, -0.425, -0.495, -0.52 , -0.535,\n",
              "        -0.58 , -0.64 , -0.635, -0.53 , -0.465, -0.345, -0.31 , -0.255,\n",
              "        -0.25 , -0.25 , -0.24 , -0.21 , -0.21 , -0.22 , -0.21 , -0.2  ,\n",
              "        -0.2  , -0.2  , -0.205, -0.185, -0.18 , -0.185, -0.185, -0.16 ,\n",
              "        -0.15 , -0.16 , -0.16 , -0.145, -0.13 , -0.125, -0.12 , -0.095,\n",
              "        -0.09 , -0.07 , -0.065, -0.035, -0.025,  0.   ,  0.01 ,  0.03 ,\n",
              "         0.045,  0.06 ,  0.075,  0.12 ,  0.125,  0.14 ,  0.15 ,  0.19 ,\n",
              "         0.2  ,  0.2  ,  0.205,  0.2  ,  0.205,  0.17 ,  0.16 ,  0.14 ,\n",
              "         0.135,  0.09 ,  0.06 ,  0.   ,  0.   ,  0.   ],\n",
              "       [ 0.   ,  0.   ,  0.   , -0.22 , -0.225, -0.235, -0.24 , -0.25 ,\n",
              "        -0.25 , -0.26 , -0.25 , -0.25 , -0.24 , -0.26 , -0.26 , -0.28 ,\n",
              "        -0.29 , -0.29 , -0.285, -0.29 , -0.305, -0.31 , -0.315, -0.325,\n",
              "        -0.35 , -0.39 , -0.42 , -0.415, -0.41 , -0.435, -0.46 , -0.49 ,\n",
              "        -0.52 , -0.53 , -0.53 , -0.555, -0.56 , -0.535, -0.53 , -0.54 ,\n",
              "        -0.525, -0.505, -0.485, -0.43 , -0.38 , -0.335, -0.3  , -0.265,\n",
              "        -0.235, -0.195, -0.18 , -0.11 , -0.08 , -0.055, -0.05 , -0.035,\n",
              "        -0.035, -0.025, -0.02 , -0.02 , -0.01 ,  0.02 ,  0.03 ,  0.055,\n",
              "         0.045,  0.07 ,  0.105,  0.185,  0.255,  0.37 ,  0.45 ,  0.595,\n",
              "         0.665,  0.91 ,  1.005,  1.04 ,  0.94 ,  0.36 , -0.035, -0.775,\n",
              "        -1.035, -1.41 , -1.565, -1.815, -1.905, -1.985, -1.985, -1.91 ,\n",
              "        -1.855, -1.655, -1.525, -1.285, -1.19 , -1.055, -0.98 , -0.825,\n",
              "        -0.71 , -0.47 , -0.38 , -0.275, -0.235, -0.13 , -0.015,  0.195,\n",
              "         0.295,  0.385,  0.38 ,  0.37 ,  0.345,  0.375,  0.4  ,  0.415,\n",
              "         0.42 ,  0.45 ,  0.465,  0.505,  0.52 ,  0.535,  0.545,  0.565,\n",
              "         0.575,  0.605,  0.635,  0.66 ,  0.695,  0.73 ,  0.75 ,  0.775,\n",
              "         0.795,  0.825,  0.845,  0.905,  0.93 ,  0.95 ,  0.955,  0.965,\n",
              "         0.985,  0.985,  0.99 ,  0.96 ,  0.945,  0.89 ,  0.885,  0.83 ,\n",
              "         0.79 ,  0.725,  0.675,  0.   ,  0.   ,  0.   ],\n",
              "       [ 0.   ,  0.   ,  0.   ,  0.36 ,  0.39 ,  0.45 ,  0.46 ,  0.445,\n",
              "         0.43 ,  0.43 ,  0.43 ,  0.43 ,  0.425,  0.365,  0.33 ,  0.28 ,\n",
              "         0.25 ,  0.19 ,  0.15 ,  0.095,  0.055,  0.01 , -0.025, -0.08 ,\n",
              "        -0.13 , -0.16 , -0.16 , -0.15 , -0.16 , -0.18 , -0.18 , -0.17 ,\n",
              "        -0.17 , -0.175, -0.17 , -0.12 , -0.09 , -0.02 ,  0.015,  0.04 ,\n",
              "         0.065,  0.115,  0.14 ,  0.155,  0.155,  0.125,  0.085, -0.02 ,\n",
              "        -0.09 , -0.2  , -0.235, -0.26 , -0.255, -0.265, -0.275, -0.315,\n",
              "        -0.32 , -0.295, -0.295, -0.305, -0.31 , -0.31 , -0.305, -0.29 ,\n",
              "        -0.295, -0.33 , -0.355, -0.385, -0.33 , -0.055,  0.22 ,  0.995,\n",
              "         1.465,  2.25 ,  2.43 ,  2.245,  1.95 ,  1.225,  0.855,  0.175,\n",
              "        -0.16 , -0.61 , -0.68 , -0.59 , -0.52 , -0.54 , -0.575, -0.645,\n",
              "        -0.685, -0.695, -0.69 , -0.695, -0.715, -0.725, -0.73 , -0.76 ,\n",
              "        -0.78 , -0.785, -0.78 , -0.77 , -0.78 , -0.785, -0.785, -0.795,\n",
              "        -0.79 , -0.78 , -0.765, -0.745, -0.72 , -0.685, -0.665, -0.65 ,\n",
              "        -0.64 , -0.6  , -0.57 , -0.485, -0.465, -0.405, -0.395, -0.34 ,\n",
              "        -0.31 , -0.245, -0.205, -0.15 , -0.115, -0.055, -0.005,  0.06 ,\n",
              "         0.09 ,  0.125,  0.165,  0.23 ,  0.255,  0.265,  0.26 ,  0.265,\n",
              "         0.265,  0.28 ,  0.29 ,  0.285,  0.275,  0.24 ,  0.22 ,  0.21 ,\n",
              "         0.2  ,  0.145,  0.125,  0.   ,  0.   ,  0.   ]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BYHzWBD8aDgT"
      },
      "source": [
        "# Creating cpp header file with weights and biases"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ncNWMdoaCFL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cafd51cb-dc5b-48f3-d876-d8f95a6a8cc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#ifndef MODEL_H\n",
            "#define MODEL_H\n",
            "\n",
            "int8_t LAYER0_KERNEL []{-65,51,29,11,-25,22,4,-7,51,17,-4,17,-41,24,65,-63,-39,-13,-4,5,-13,35,37,-42,31,46,-8,-21,18,-2,37,-31,30,-4,25,17,-48,31,27,52,-48,-33,27,18,19,18,42,53,-12,51,-40,15,47,-25,41,26,-23,-35,-20,-31,-64,-15,40,-60,-28,-44,-21,39,54,46,-56,55,-20,-27,-29,-39,-47,14,-6,8,-45,49,42,27,19,21,27,-6,18,49,13,50,33,3,-29,-64,-31,-56,1,60,33,32,-36,0,44,11,-61,22,58,47,-1,-26,-68,21,22,-11,-57,33,-40,31,15,7,-52,9,-9,-67,-11,-1,60,-17,-10,-28,41,-28,6,-28,10,35,53,0,-47,-43,-42,-62,68,-47,-53,45,10,-34,32,69,-37,-14,23,25,-26,-40,24,-47,12,-7,-21,-36,50,-38,-42,-28,27,-45,8,43,28,27,59,-2,36,54,8,-35,-21,57,-1,35,-5,-33,-53,-12,13,-44,-8,7,19,39,-9,46,2,-11,21,-69,-12,27,12,-42,54,-46,-30,18,5,-27,25,24,4,-40,-2,-35,-10,25,71,-68,-27,-5,-14,0,-13,-14,-42,6,53,-10,-49,63,-31,-56,17,-16,8,67,49,-64,40,-37,14,-51,37,-9,-25,17,32,33,38,-3,-4,15,-1,19,-3,22,15,-15,-56,-4,-17,8,68,12,-14,70,9,46,-8,50,1,43,57,-55,-15,32,25,1,-57,32,34,28,38,-63,45,12,-39,31,-68,49,43,-67,70,6,-3,28,-20,-60,42,50,-69,-60,27,26,-61,43,-5,-35,-9,-45,-21,-46,42,-6,38,67,-43,-43,15,-34,-1,31,66,-38,24,52,6,-9,-19,42,-6,-59,25,-26,-6,-19,-30,-18,15,47,-37,-9,43,-14,45,72,35,-53,-40,-12,30,11,53,-60,-17,-9,-4,-26,-30,32,13,-59,1,36,-59,-34,-31,10,-24,-45,-70,-23,25,-8,-41,5,2,-30,-20,69,-21,48,64,-64,-13,-38,1,-14,40,-7,30,35,-33,-15,-36,13,-32,-4,34,36,-65,-67,37,21,19,-29,-4,-20,-1,-8,-2,-56,71,27,34,-45,-22,-50,-49,14,-45,-70,-34,-64,-38,-28,18,30,-57,60,44,37,11,-33,-11,19,-28,-22,-15,45,-28,-25,14,47,-1,27,16,35,-6,46,-60,19,-46,38,39,-44,-9,-34,47,-5,28,26,50,-28,45,56,-16,22,-23,-41,-23,4,57,41,-32,6,-34,49,17,21,-6,-65,26,-20,-19,28,4,-25,37,50,27,8,44,-2,-53,21,62,58,-58,-43,26,-25,17,-12,38,-67,-9,-47,-46,0,-31,-32,9,41,23,-65,-10,14,-39,-8,-32,19,3,-45,-28,-41,-35,62,-68,10,46,-39,48,41,19,25,27,21,2,28,-66,36,5,-43,-59,11,-38,24,16,6,-66,28,-9,62,38,-20,33,26,-14,57,7,0,53,47,35,-9,-11,71,-46,26,-58,38,-26,11,29,-32,67,-10,-50,71,-18,45,58,51,3,-56,49,-25,35,-47,-52,-63,-12,14,25,1,-18,-18,-22,-20,-37,-14,-5,-1,15,-67,33,-38};\n",
            "int8_t LAYER0_BIAS []{-13,13,-13,-11,14,-11,-12,15,14,-13};\n",
            "\n",
            "int8_t LAYER1_KERNEL []{-108,6,-65,-60,127,-75,-1,-127,-16,77,44,61,-15,21,-17,-117,126,39,-27,-127,-5,0,59,-66,-27,88,-56,-85,99,93,-108,-51,6,-87,13,15,-92,-127,101,103};\n",
            "int8_t LAYER1_BIAS []{13,-13,-14,-15};\n",
            "\n",
            "float SAMPLE_INPUT_N[]={0.000000,0.000000,0.000000,-0.240000,-0.215000,-0.170000,-0.165000,-0.155000,-0.140000,-0.110000,-0.130000,-0.160000,-0.170000,-0.190000,-0.215000,-0.215000,-0.235000,-0.280000,-0.320000,-0.370000,-0.385000,-0.420000,-0.435000,-0.485000,-0.500000,-0.505000,-0.515000,-0.525000,-0.535000,-0.555000,-0.550000,-0.525000,-0.520000,-0.495000,-0.500000,-0.440000,-0.405000,-0.325000,-0.300000,-0.275000,-0.260000,-0.230000,-0.215000,-0.220000,-0.230000,-0.315000,-0.335000,-0.415000,-0.470000,-0.525000,-0.565000,-0.605000,-0.605000,-0.595000,-0.585000,-0.585000,-0.585000,-0.580000,-0.585000,-0.600000,-0.610000,-0.600000,-0.610000,-0.590000,-0.580000,-0.580000,-0.600000,-0.650000,-0.625000,-0.385000,-0.170000,0.450000,0.830000,1.610000,1.910000,1.990000,1.765000,0.975000,0.540000,-0.230000,-0.505000,-0.970000,-1.125000,-1.190000,-1.090000,-0.865000,-0.780000,-0.770000,-0.800000,-0.875000,-0.900000,-0.900000,-0.885000,-0.905000,-0.905000,-0.930000,-0.930000,-0.920000,-0.935000,-0.955000,-0.980000,-0.995000,-0.985000,-0.970000,-0.955000,-0.960000,-0.965000,-0.975000,-0.980000,-0.945000,-0.915000,-0.900000,-0.885000,-0.845000,-0.820000,-0.775000,-0.765000,-0.735000,-0.700000,-0.610000,-0.560000,-0.480000,-0.445000,-0.390000,-0.355000,-0.280000,-0.235000,-0.180000,-0.145000,-0.050000,-0.010000,0.045000,0.075000,0.085000,0.080000,0.145000,0.165000,0.175000,0.165000,0.135000,0.115000,0.085000,0.075000,0.045000,0.030000,-0.005000,-0.025000,0.000000,0.000000,0.000000};\n",
            "float SAMPLE_INPUT_S[]={0.000000,0.000000,0.000000,-0.120000,-0.125000,-0.095000,-0.075000,-0.045000,-0.030000,-0.020000,-0.010000,0.020000,0.015000,0.025000,0.030000,0.050000,0.025000,0.000000,-0.015000,-0.025000,-0.020000,-0.015000,-0.005000,0.050000,0.045000,0.035000,0.030000,0.020000,0.020000,-0.010000,-0.035000,-0.060000,-0.060000,-0.110000,-0.130000,-0.145000,-0.160000,-0.195000,-0.205000,-0.200000,-0.210000,-0.235000,-0.245000,-0.235000,-0.225000,-0.250000,-0.255000,-0.245000,-0.240000,-0.245000,-0.260000,-0.240000,-0.235000,-0.260000,-0.265000,-0.245000,-0.240000,-0.245000,-0.255000,-0.240000,-0.235000,-0.245000,-0.255000,-0.230000,-0.230000,-0.235000,-0.225000,-0.185000,-0.160000,-0.100000,-0.045000,0.110000,0.170000,0.250000,0.245000,0.245000,0.210000,0.000000,-0.055000,-0.030000,-0.085000,-0.290000,-0.345000,-0.395000,-0.425000,-0.495000,-0.520000,-0.535000,-0.580000,-0.640000,-0.635000,-0.530000,-0.465000,-0.345000,-0.310000,-0.255000,-0.250000,-0.250000,-0.240000,-0.210000,-0.210000,-0.220000,-0.210000,-0.200000,-0.200000,-0.200000,-0.205000,-0.185000,-0.180000,-0.185000,-0.185000,-0.160000,-0.150000,-0.160000,-0.160000,-0.145000,-0.130000,-0.125000,-0.120000,-0.095000,-0.090000,-0.070000,-0.065000,-0.035000,-0.025000,0.000000,0.010000,0.030000,0.045000,0.060000,0.075000,0.120000,0.125000,0.140000,0.150000,0.190000,0.200000,0.200000,0.205000,0.200000,0.205000,0.170000,0.160000,0.140000,0.135000,0.090000,0.060000,0.000000,0.000000,0.000000};\n",
            "float SAMPLE_INPUT_V[]={0.000000,0.000000,0.000000,-0.220000,-0.225000,-0.235000,-0.240000,-0.250000,-0.250000,-0.260000,-0.250000,-0.250000,-0.240000,-0.260000,-0.260000,-0.280000,-0.290000,-0.290000,-0.285000,-0.290000,-0.305000,-0.310000,-0.315000,-0.325000,-0.350000,-0.390000,-0.420000,-0.415000,-0.410000,-0.435000,-0.460000,-0.490000,-0.520000,-0.530000,-0.530000,-0.555000,-0.560000,-0.535000,-0.530000,-0.540000,-0.525000,-0.505000,-0.485000,-0.430000,-0.380000,-0.335000,-0.300000,-0.265000,-0.235000,-0.195000,-0.180000,-0.110000,-0.080000,-0.055000,-0.050000,-0.035000,-0.035000,-0.025000,-0.020000,-0.020000,-0.010000,0.020000,0.030000,0.055000,0.045000,0.070000,0.105000,0.185000,0.255000,0.370000,0.450000,0.595000,0.665000,0.910000,1.005000,1.040000,0.940000,0.360000,-0.035000,-0.775000,-1.035000,-1.410000,-1.565000,-1.815000,-1.905000,-1.985000,-1.985000,-1.910000,-1.855000,-1.655000,-1.525000,-1.285000,-1.190000,-1.055000,-0.980000,-0.825000,-0.710000,-0.470000,-0.380000,-0.275000,-0.235000,-0.130000,-0.015000,0.195000,0.295000,0.385000,0.380000,0.370000,0.345000,0.375000,0.400000,0.415000,0.420000,0.450000,0.465000,0.505000,0.520000,0.535000,0.545000,0.565000,0.575000,0.605000,0.635000,0.660000,0.695000,0.730000,0.750000,0.775000,0.795000,0.825000,0.845000,0.905000,0.930000,0.950000,0.955000,0.965000,0.985000,0.985000,0.990000,0.960000,0.945000,0.890000,0.885000,0.830000,0.790000,0.725000,0.675000,0.000000,0.000000,0.000000};\n",
            "float SAMPLE_INPUT_F[]={0.000000,0.000000,0.000000,0.360000,0.390000,0.450000,0.460000,0.445000,0.430000,0.430000,0.430000,0.430000,0.425000,0.365000,0.330000,0.280000,0.250000,0.190000,0.150000,0.095000,0.055000,0.010000,-0.025000,-0.080000,-0.130000,-0.160000,-0.160000,-0.150000,-0.160000,-0.180000,-0.180000,-0.170000,-0.170000,-0.175000,-0.170000,-0.120000,-0.090000,-0.020000,0.015000,0.040000,0.065000,0.115000,0.140000,0.155000,0.155000,0.125000,0.085000,-0.020000,-0.090000,-0.200000,-0.235000,-0.260000,-0.255000,-0.265000,-0.275000,-0.315000,-0.320000,-0.295000,-0.295000,-0.305000,-0.310000,-0.310000,-0.305000,-0.290000,-0.295000,-0.330000,-0.355000,-0.385000,-0.330000,-0.055000,0.220000,0.995000,1.465000,2.250000,2.430000,2.245000,1.950000,1.225000,0.855000,0.175000,-0.160000,-0.610000,-0.680000,-0.590000,-0.520000,-0.540000,-0.575000,-0.645000,-0.685000,-0.695000,-0.690000,-0.695000,-0.715000,-0.725000,-0.730000,-0.760000,-0.780000,-0.785000,-0.780000,-0.770000,-0.780000,-0.785000,-0.785000,-0.795000,-0.790000,-0.780000,-0.765000,-0.745000,-0.720000,-0.685000,-0.665000,-0.650000,-0.640000,-0.600000,-0.570000,-0.485000,-0.465000,-0.405000,-0.395000,-0.340000,-0.310000,-0.245000,-0.205000,-0.150000,-0.115000,-0.055000,-0.005000,0.060000,0.090000,0.125000,0.165000,0.230000,0.255000,0.265000,0.260000,0.265000,0.265000,0.280000,0.290000,0.285000,0.275000,0.240000,0.220000,0.210000,0.200000,0.145000,0.125000,0.000000,0.000000,0.000000};\n",
            "\n",
            "float dequantize(int8_t x_q)\n",
            "{\n",
            "    return 0.004901f * x_q;\n",
            "}\n",
            "\n",
            "#endif\n"
          ]
        }
      ],
      "source": [
        "header  = \"#ifndef MODEL_H\\n\"\n",
        "header += \"#define MODEL_H\\n\\n\"\n",
        "for i, layer in enumerate(model.layers):\n",
        "  kernel = layer.weights[0].numpy()\n",
        "  bias = layer.weights[1].numpy()\n",
        "\n",
        "  if len(kernel.shape)<2:\n",
        "    kernel = kernel.reshape(1, kernel.shape[0])\n",
        "  if len(bias.shape)<2:\n",
        "    bias = bias.reshape(1, bias.shape[0])\n",
        "\n",
        "  kernel_lens = []\n",
        "\n",
        "  # Not saving bias lens as they are the same as matmul(input, kernel) matrix after the reshaping above\n",
        "  for j, shape in enumerate(kernel.shape):\n",
        "    string = \"LAYER%d_KERNEL_DIM%d\"%(i, j)\n",
        "    kernel_lens.append(string)\n",
        "\n",
        "  header += \"int8_t LAYER%d_KERNEL []{\"%(i)\n",
        "\n",
        "  for k in kernel.flatten():\n",
        "    header += \"%d,\"%(k)\n",
        "\n",
        "  header = header[:-1] #removing the last comma\n",
        "  header+=\"};\\n\"\n",
        "\n",
        "  header += \"int8_t LAYER%d_BIAS []{\"%(i)\n",
        "\n",
        "  for m in bias.flatten():\n",
        "    header += \"%d,\"%(m)\n",
        "\n",
        "  header=header[:-1] #removing the last comma\n",
        "  header+=\"};\\n\\n\"\n",
        "\n",
        "for i, sym in enumerate(all_types):\n",
        "  #Adding sample in\n",
        "  header+= f\"float SAMPLE_INPUT_{sym}\"+\"[]={\"\n",
        "  for i in input_samps[i].flatten():\n",
        "    header+=\"%f,\"%i\n",
        "  header=header[:-1]\n",
        "  header+=\"};\\n\"\n",
        "\n",
        "\n",
        "header += \"\"\"\n",
        "float dequantize(int8_t x_q)\n",
        "{\n",
        "    return %ff * x_q;\n",
        "}\n",
        "\n",
        "\"\"\"%(scale)\n",
        "\n",
        "header += \"#endif\"\n",
        "print(header)\n",
        "f = open(\"%s/model.h\"% (PATH), \"w\")\n",
        "f.write(header)\n",
        "f.close()\n",
        "# https://www.geeksforgeeks.org/pass-2d-array-parameter-c/\n",
        "# https://stackoverflow.com/questions/15283523/reshaping-a-1-d-array-to-a-multidimensional-array"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model comparision"
      ],
      "metadata": {
        "id": "BhlidIRRhYMC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def check_model(model):\n",
        "  max = -np.inf\n",
        "  min = np.inf\n",
        "  for i, v in enumerate(model.trainable_variables):\n",
        "    nmax = np.amax(model.trainable_variables[i].numpy())\n",
        "    max = max if max > nmax else nmax\n",
        "\n",
        "    nmin = np.amin(model.trainable_variables[i].numpy())\n",
        "    min = min if min < nmin else nmin\n",
        "\n",
        "  alpha = -max\n",
        "  beta = max\n",
        "\n",
        "  alpha_q = -127\n",
        "  beta_q = 127\n",
        "\n",
        "  scale = (beta - alpha)/(beta_q - alpha_q)\n",
        "  zero = (alpha * beta_q - beta * alpha_q) / (beta - alpha)\n",
        "\n",
        "  quantize = lambda x, scale=scale, zero=zero, alpha_q=alpha_q, beta_q=beta_q : round((x/scale) - zero) if x >= alpha_q and x <= beta_q else (alpha_q if x < alpha_q else beta_q)\n",
        "  dequantize = lambda x_q, scale=scale, zero=zero: scale * (x_q + zero)\n",
        "\n",
        "  weight_sample = []\n",
        "  rindices = []\n",
        "\n",
        "\n",
        "  for i, v in enumerate(model.trainable_variables):\n",
        "    arr = model.trainable_variables[i].numpy()\n",
        "    randomi = np.random.randint(0, len(arr.flatten()))\n",
        "    rindices.append(randomi)\n",
        "    weight_sample.append(arr.flatten()[randomi])\n",
        "\n",
        "  loss, acc = model.evaluate(test_ds)\n",
        "  print('Regular model, accuracy: %.60g'%(100 * acc), \"%\")\n",
        "  print('Weight samples ', [round(j,2) for j in weight_sample])\n",
        "\n",
        "  weight_sample = []\n",
        "\n",
        "  for i, v in enumerate(model.trainable_variables):\n",
        "    arr = model.trainable_variables[i].numpy()\n",
        "    arr = np.vectorize(quantize)(arr)\n",
        "    weight_sample.append(arr.flatten()[rindices[i]])\n",
        "    model.trainable_variables[i].assign(arr)\n",
        "\n",
        "  loss, acc = model.evaluate(test_ds)\n",
        "  print('Quantized model, accuracy: %.60g'%(100 * acc), \"%\")\n",
        "  print('Weight samples ', weight_sample)\n",
        "\n",
        "  weight_sample = []\n",
        "\n",
        "  for i, v in enumerate(model.trainable_variables):\n",
        "    arr = model.trainable_variables[i].numpy()\n",
        "    arr = np.vectorize(dequantize)(arr)\n",
        "    weight_sample.append(arr.flatten()[rindices[i]])\n",
        "    model.trainable_variables[i].assign(arr)\n",
        "\n",
        "  loss, acc = model.evaluate(test_ds)\n",
        "  print('Dequantized model, accuracy: %.60g'%(100 * acc), \"%\")\n",
        "  print('Weight samples = ', [round(j,2) for j in weight_sample])\n",
        "  return scale, zero, alpha_q, beta_q, alpha, beta\n",
        "\n",
        "\n",
        "model_names = ['sigmoid_plus_sigmoid', 'relu_plus_sigmoid' , 'relu_plus_softmax', 'sigmoid_plus_softmax']\n",
        "\n",
        "! mkdir model_viz\n",
        "for name in model_names:\n",
        "  try:\n",
        "    MODEL_DIR = f'/content/{name}.h5'\n",
        "    print(f'\\n\\nChecking {name}...')\n",
        "\n",
        "    # model = models.load_model(MODEL_DIR)\n",
        "    # scale, zero, alpha_q, beta_q, alpha, beta = check_model(model)\n",
        "\n",
        "    model = models.load_model(MODEL_DIR)\n",
        "    scale, zero, alpha_q, beta_q, alpha, beta = check_model(model)\n",
        "    print(alpha,beta, alpha_q, beta_q, scale)\n",
        "    # tf.keras.utils.plot_model(\n",
        "    # to_file=f'/content/model_viz/model_{name}.png',\n",
        "    # model = model,\n",
        "    # show_shapes=True,\n",
        "    # show_dtype=True,\n",
        "    # show_layer_names=True,\n",
        "    # rankdir='TB',\n",
        "    # expand_nested=True,\n",
        "    # dpi=600,\n",
        "    # layer_range=None,\n",
        "    # show_layer_activations=True\n",
        "    # )\n",
        "  except:\n",
        "    pass\n",
        "\n",
        "# print(scale, zero, alpha_q, beta_q, alpha, beta)"
      ],
      "metadata": {
        "id": "lffcCIJggHjs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64f6f81c-fb3c-4c0c-8bb3-c14b697f15f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Checking sigmoid_plus_sigmoid...\n",
            "\n",
            "\n",
            "Checking relu_plus_sigmoid...\n",
            "\n",
            "\n",
            "Checking relu_plus_softmax...\n",
            "\n",
            "\n",
            "Checking sigmoid_plus_softmax...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " # Output tables of classification metrics in latex per model"
      ],
      "metadata": {
        "id": "wEo3lyYFCO3T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "def quantize(model):\n",
        "\n",
        "  scale = (beta - alpha)/(beta_q - alpha_q)\n",
        "  zero = (alpha * beta_q - beta * alpha_q) / (beta - alpha)\n",
        "\n",
        "  quantize = lambda x, scale=scale, zero=zero, alpha_q=alpha_q, beta_q=beta_q : round((x/scale) - zero) if x >= alpha_q and x <= beta_q else (alpha_q if x < alpha_q else beta_q)\n",
        "  dequantize = lambda x_q, scale=scale, zero=zero: scale * (x_q + zero)\n",
        "\n",
        "  for i, v in enumerate(model.trainable_variables):\n",
        "    arr = model.trainable_variables[i].numpy()\n",
        "    arr = np.vectorize(quantize)(arr)\n",
        "    model.trainable_variables[i].assign(arr)\n",
        "\n",
        "  return model\n",
        "\n",
        "def dequantize(model):\n",
        "\n",
        "\n",
        "  scale = (beta - alpha)/(beta_q - alpha_q)\n",
        "  zero = (alpha * beta_q - beta * alpha_q) / (beta - alpha)\n",
        "\n",
        "  quantize = lambda x, scale=scale, zero=zero, alpha_q=alpha_q, beta_q=beta_q : round((x/scale) - zero) if x >= alpha_q and x <= beta_q else (alpha_q if x < alpha_q else beta_q)\n",
        "  dequantize = lambda x_q, scale=scale, zero=zero: scale * (x_q + zero)\n",
        "\n",
        "  for i, v in enumerate(model.trainable_variables):\n",
        "    arr = model.trainable_variables[i].numpy()\n",
        "    arr = np.vectorize(dequantize)(arr)\n",
        "    model.trainable_variables[i].assign(arr)\n",
        "\n",
        "  return model\n",
        "\n",
        "\n",
        "def latex_with_lines(df, *args, **kwargs):\n",
        "  kwargs['column_format'] = '|'.join([''] + ['c'] * df.index.nlevels\n",
        "                                          + ['c'] * df.shape[1] + [''])\n",
        "  res = df.to_latex(*args, **kwargs)\n",
        "  for r in ['\\\\toprule', '\\\\midrule', '\\\\bottomrule']:\n",
        "    if r == '\\\\bottomrule':\n",
        "      res = res.replace(r, '')\n",
        "    else:\n",
        "      res = res.replace(r, '\\\\hline')\n",
        "\n",
        "  res = res.replace('\\\\\\\\\\n', '\\\\\\\\ \\n\\\\hline\\n')\n",
        "  res = \"\"\"\\\\begin{table}[htb]\n",
        "  \\\\Large\n",
        "  \\\\caption{}\n",
        "  \\\\resizebox{\\columnwidth}{!}{\n",
        "  \\\\renewcommand{\\\\arraystretch}{3}\n",
        "  \"\"\" + res + \"\"\"\n",
        "      }\n",
        "  \\\\end{table}\n",
        "  \"\"\"\n",
        "\n",
        "  return res\n",
        "\n",
        "\n",
        "\n",
        "for name in model_names:\n",
        "  MODEL_DIR = f'/content/{name}.h5'\n",
        "  model = models.load_model(MODEL_DIR)\n",
        "  print(name)\n",
        "  y_preds = model.predict(x_test, batch_size=batch, verbose=1)\n",
        "  y_preds = np.argmax(y_preds, axis=2)\n",
        "  y_test_t = np.argmax(y_test, axis=2)\n",
        "  report = classification_report(y_test_t, y_preds, target_names = all_types,output_dict=True)\n",
        "  report = pd.DataFrame.from_dict(report)\n",
        "  print(latex_with_lines(report))\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "4S5I6juj0Bv7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "364014f7-c56a-4ad1-848c-5937657875ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-1e5bb5edb037>\u001b[0m in \u001b[0;36m<cell line: 60>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel_names\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m   \u001b[0mMODEL_DIR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'/content/{name}.h5'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m   \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMODEL_DIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0my_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/saving/saving_api.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, safe_mode, **kwargs)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[0;31m# Legacy case.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m     return legacy_sm_saving_lib.load_model(\n\u001b[0m\u001b[1;32m    213\u001b[0m         \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/saving/legacy/save.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, options)\u001b[0m\n\u001b[1;32m    228\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m                             raise IOError(\n\u001b[0m\u001b[1;32m    231\u001b[0m                                 \u001b[0;34mf\"No file or directory found at {filepath_str}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m                             )\n",
            "\u001b[0;31mOSError\u001b[0m: No file or directory found at /content/sigmoid_plus_sigmoid.h5"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ROC Curve"
      ],
      "metadata": {
        "id": "vE0-TT7PCkDC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import RocCurveDisplay\n",
        "\n",
        "y_preds = model.predict(x_test, batch_size=batch, verbose=1)\n",
        "\n",
        "RocCurveDisplay.from_predictions(\n",
        "    y_test[:, 0],\n",
        "    y_preds[:, 0],\n",
        "    name=f\"N vs the rest\",\n",
        "    color=\"darkorange\",\n",
        ")\n",
        "plt.plot([0, 1], [0, 1], \"k--\", label=\"chance level (AUC = 0.5)\")\n",
        "plt.axis(\"square\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"One-vs-Rest ROC curves:\\nVirginica vs (Setosa & Versicolor)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "v7oq8A4JJaCX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Joz9dGTFXwJi"
      },
      "source": [
        "# Quantization aware training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uCZN65jcm8ly"
      },
      "outputs": [],
      "source": [
        "# !pip install -q tensorflow-model-optimization\n",
        "\n",
        "# import tensorflow_model_optimization as tfmot\n",
        "\n",
        "# quantize_model = tfmot.quantization.keras.quantize_model\n",
        "\n",
        "# # q_aware stands for for quantization aware.\n",
        "# model = quantize_model(model)\n",
        "\n",
        "# # `quantize_model` requires a recompile.\n",
        "# model.compile(optimizer=optimizer,\n",
        "#               loss=loss_object,\n",
        "#               metrics=['accuracy'])\n",
        "\n",
        "# model.fit(\n",
        "#       train_ds,\n",
        "#       epochs = 5,\n",
        "#       verbose = 1,\n",
        "#       validation_data = test_ds)\n",
        "# print(model.summary())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5JcMWnnrnVmC"
      },
      "outputs": [],
      "source": [
        "# import pathlib\n",
        "# converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "# # converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]\n",
        "# # converter.experimental_new_converter =True\n",
        "# # converter.allow_custom_ops=True\n",
        "# #converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n",
        "# #converter.target_spec.supported_types = [tf.float16]\n",
        "# tflite_model = converter.convert()\n",
        "# tflite_model_dir = '/content/model_lite.tflite'\n",
        "# tflite_model_file = pathlib.Path(tflite_model_dir)\n",
        "# size = tflite_model_file.write_bytes(tflite_model)\n",
        "# print('%s KB'% (size/(10**3)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DBGNYrK9neAR"
      },
      "outputs": [],
      "source": [
        "# import numpy as np\n",
        "# # Load TFLite model and allocate tensors.\n",
        "# TFLITE_MODEL_FILE = '/tmp/model1.tflite'\n",
        "# interpreter = tf.lite.Interpreter(model_path=tflite_model_dir)\n",
        "# interpreter.allocate_tensors()\n",
        "# print(interpreter.get_input_details())\n",
        "# input_index = interpreter.get_input_details()[0][\"index\"]\n",
        "# output_index = interpreter.get_output_details()[0][\"index\"]\n",
        "# predictions = []\n",
        "# validation_gen = test_ds.as_numpy_iterator()\n",
        "# batch = next(validation_gen,None)\n",
        "\n",
        "# while (batch!=None):\n",
        "#   for i in range(len(batch[0])):\n",
        "#     img = batch[0][i]\n",
        "#     img = np.expand_dims(img, axis=0).astype(np.float32)\n",
        "#     label = batch[1][i]\n",
        "#     interpreter.set_tensor(input_index, img)\n",
        "#     interpreter.invoke()\n",
        "#     output = interpreter.get_tensor(output_index)\n",
        "#     predictions.append((np.argmax(output[0]) == np.argmax(label)))\n",
        "#   batch = next(validation_gen,None)\n",
        "# print(\"%s out of %d was correct\" % (predictions.count(True), len(predictions)))\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}